In Study 1, adults with strong religious commitments and faith practices were interviewed in depth about their spiritual experiences and their understanding of the mind by experienced ethnographers. These interviews yielded one measure of porosity (“Porosity Vignettes”), one measure of absorption (the Absorption scale), and one measure of spiritual experience (“Spiritual Events” scale).

This notebook contains all of the analyses for Study 1 (revised July 2020).

source("../scripts_general/dependencies.R")
package ‘ggplot2’ was built under R version 4.0.2package ‘tibble’ was built under R version 4.0.2package ‘dplyr’ was built under R version 4.0.2package ‘stargazer’ was built under R version 4.0.1package ‘kableExtra’ was built under R version 4.0.2package ‘htmltools’ was built under R version 4.0.2package ‘svglite’ was built under R version 4.0.2
source("../scripts_general/custom_funs.R")
source("../scripts_general/var_recode_contrast.R")
source("../scripts_general/data_load.R")
# get item-level question
d1_byq <- read_csv("./data_byquestion/study1_byquestion.csv")
Parsed with column specification:
cols(
  .default = col_double(),
  study = col_character(),
  date = col_date(format = ""),
  researcher = col_character(),
  country = col_character(),
  site = col_character(),
  religion = col_character(),
  subject_gender = col_character(),
  subject_job = col_character(),
  subject_schedule = col_character(),
  subject_livedhere = col_character(),
  subject_lang = col_character(),
  subject_marital = col_character(),
  subject_hs = col_character(),
  subject_liveswith = col_character(),
  servicesperweek = col_character()
)
See spec(...) for full column specifications.
d3_byq <- read_csv("./data_byquestion/packets123_data_byquestion_long.csv") %>%
  filter(packet == 1)
Parsed with column specification:
cols(
  packet = col_double(),
  version = col_double(),
  subj = col_double(),
  ctry = col_character(),
  wher = col_character(),
  recr = col_character(),
  whoc = col_character(),
  question = col_character(),
  response = col_double()
)
d4_byq <- read_csv("./data_byquestion/study4_byquestion.csv")
Missing column names filled in: 'X1' [1]Parsed with column specification:
cols(
  .default = col_double(),
  p7_ctry = col_character(),
  p7_abs_check = col_character(),
  p7_dse_check = col_character(),
  p7_se_check = col_character(),
  p7_unev_check = col_character(),
  p7_exsen_check = col_character(),
  p7_por_check = col_character(),
  p7_mm_check = col_character(),
  p7_dem_sex = col_character(),
  p7_dem_pocc = col_character(),
  p7_dem_major = col_character(),
  p7_dem_ethnicity = col_character(),
  p7_dem_rur.urb = col_character(),
  p7_dem_affrd.basics = col_character(),
  p7_dem_religion = col_character(),
  p7_dem_church = col_character(),
  p7_dem_holy.tung.gif = col_character(),
  p7_abs_child.exp_cat = col_logical(),
  p7_abs_poetic_cat = col_logical(),
  p7_abs_tv.real_cat = col_logical()
  # ... with 162 more columns
)
See spec(...) for full column specifications.
# dataframe with variable names optimized for stargazer tables
d1_fortab <- d1 %>%
  mutate_at(vars(ends_with("_score"), subject_age), scale) %>%
  mutate(subject_hs = case_when(is.na(subject_hs) ~ NA_character_,
                                grepl("yes", tolower(subject_hs)) ~ "yes",
                                TRUE ~ "no"),
         subject_hs = factor(subject_hs, 
                             levels = c("no", "yes")),
         subject_gender = factor(subject_gender, 
                                 levels = c("Female", "Male"))) %>%
  rename("Spiritual Events" = "spev_score",
         "Spiritual Events, recoded" = "spev_recoded_score",
         "Porosity Vignettes" = "pv_score",
         "Absorption" = "abs_score",
         "Country" = "country",
         "Site" = "site",
         "Religion" = "religion",
         "Gender" = "subject_gender",
         "Age" = "subject_age",
         "Education" = "subject_hs")
# set contrasts
contrasts(d1_fortab$Country) <- cbind(": Gh." = c(-1, 1, 0, 0, 0),
                                      ": Th." = c(-1, 0, 1, 0, 0),
                                      ": Ch." = c(-1, 0, 0, 1, 0),
                                      ": Va." = c(-1, 0, 0, 0, 1))
contrasts(d1_fortab$Site) <- cbind(": rural" = c(-1, 1))
contrasts(d1_fortab$Religion) <- cbind(": CC" = c(1, -1))
contrasts(d1_fortab$Education) <- cbind(": at least HS" = c(-1, 1))
contrasts(d1_fortab$Gender) <- cbind(": male" = c(-1, 1))
# dataframe with variable names optimized for stargazer tables
d3_fortab <- d3 %>%
  mutate_at(vars(ends_with("_score"), subject_age), scale) %>%
  mutate(subject_gender = factor(subject_gender, 
                                 levels = c("female", "male"))) %>%
  rename("Spiritual Events" = "spev_score",
         "Daily Spiritual Experiences" = "dse_score",
         "Absorption" = "abs_score",
         "Country" = "country",
         "Gender" = "subject_gender",
         "Age" = "subject_age")
# set contrasts
contrasts(d3_fortab$Country) <- cbind(": Gh." = c(-1, 1, 0, 0, 0),
                                      ": Th." = c(-1, 0, 1, 0, 0),
                                      ": Ch." = c(-1, 0, 0, 1, 0),
                                      ": Va." = c(-1, 0, 0, 0, 1))
contrasts(d3_fortab$Gender) <- cbind(": male" = c(-1, 1))
# dataframe with variable names optimized for stargazer tables
d4_fortab <- d4 %>%
  mutate_at(vars(ends_with("_score"), subject_age), scale) %>%
  mutate(subject_gender = factor(subject_gender, 
                                 levels = c("female", "male"))) %>%
  rename("Spiritual Events" = "spev_score",
         "Daily Spiritual Experience" = "dse_score",
         "Porosity Vignettes" = "pv_score",
         "Porosity Scale" = "por_score",
         "Absorption" = "abs_score",
         "Hallucinations" = "hall_score",
         "Paranormal" = "para_score",
         "Need for Cognition" = "cog_score",
         "Sense of Control" = "ctl_score",
         "Country" = "country",
         "Gender" = "subject_gender",
         "Age" = "subject_age")
# set contrasts
contrasts(d4_fortab$Country) <- cbind(": Gh." = c(-1, 1, 0, 0, 0),
                                      ": Th." = c(-1, 0, 1, 0, 0),
                                      ": Ch." = c(-1, 0, 0, 1, 0),
                                      ": Va." = c(-1, 0, 0, 0, 1))
contrasts(d4_fortab$Gender) <- cbind(": male" = c(-1, 1))

Omitting two presence items

“I can often somehow sense the presence of another person before I actually see or hear her/him.” “At times I somehow feel the presence of someone who is not physically there”

Study 1

d1_abs_omit <- d1_byq %>%
  select(subject_id, country, ends_with("_abs")) %>%
  select(-sensepresence_abs, -invisiblepresence_abs) %>%
  gather(question, response, -subject_id, -country) %>%
  group_by(subject_id, country) %>%
  summarise(Absorption = mean(response, na.rm = T)) %>%
  ungroup() %>%
  group_by(country) %>%
  mutate(Absorption = scale(Absorption)) %>%
  ungroup() %>%
  distinct(subject_id, Absorption) %>%
  right_join(d1_fortab %>% select(-Absorption)) %>%
  filter(subject_id %in% d1_fortab$subject_id)
`summarise()` has grouped output by 'subject_id'. You can override using the `.groups` argument.
Joining, by = "subject_id"

Table S20. Study 1, Spiritual Events: relationships with Porosity Vignettes and Absorption (sample as fixed effect)

r04_spev <- lm(`Spiritual Events` ~ `Porosity Vignettes`,
               data = d1_abs_omit)

r05_spev <- lm(`Spiritual Events` ~ Absorption,
               data = d1_abs_omit)

r06_spev <- lm(`Spiritual Events` ~ `Porosity Vignettes` * Absorption,
               data = d1_abs_omit)

r07_spev <- lm(`Spiritual Events` ~ Country * Site * Religion 
               + `Porosity Vignettes` + Country:`Porosity Vignettes`,
               data = d1_abs_omit)

r08_spev <- lm(`Spiritual Events` ~ Country * Site * Religion 
               + Absorption + Country:Absorption,
               data = d1_abs_omit)

r09_spev <- lm(`Spiritual Events` ~ Country * Site * Religion 
               + `Porosity Vignettes` + Country:`Porosity Vignettes`
               + Absorption + Country:Absorption
               + `Porosity Vignettes`:Absorption 
               + Country:`Porosity Vignettes`:Absorption,
               data = d1_abs_omit)

r10_spev <- lm(`Spiritual Events` ~ Country * Site * Religion 
               + `Porosity Vignettes` + Country:`Porosity Vignettes`
               + Absorption + Country:Absorption
               + `Porosity Vignettes`:Absorption
               + Country:`Porosity Vignettes`:Absorption
               + Gender + Age + Education,
               data = d1_abs_omit)
table_s20 <- stargazer(r04_spev, r05_spev, r06_spev, 
                       r07_spev, r08_spev, r09_spev, r10_spev,
                       type = "html",
                       ci = T,
                       report = c("vcsp"),
                       digits = 2,
                       digits.extra = 1000,
                       omit.table.layout = "n",
                       order = c("^`Porosity Vignettes`$",
                                 "^Absorption$",
                                 "^`Porosity Vignettes`:Absorption$"),
                       # dep.var.labels.include = F,
                       dep.var.caption = "",
                       single.row = T)
cat(sub_interact_fun(table_s20), 
    file = "./stargazer_tables/table_s20.html")
htmltools::includeHTML("./stargazer_tables/table_s20.html")
Spiritual Events
(1)(2)(3)(4)(5)(6)(7)
Porosity Vignettes0.36 (0.26, 0.47)0.33 (0.22, 0.43)0.36 (0.23, 0.48)0.32 (0.18, 0.47)0.30 (0.16, 0.44)
p = 0.00p = 0.00p = 0.0000001p = 0.00002p = 0.0001
Absorption0.19 (0.08, 0.30)0.13 (0.02, 0.24)0.27 (0.17, 0.36)0.23 (0.12, 0.35)0.24 (0.12, 0.36)
p = 0.001p = 0.02p = 0.0000001p = 0.0002p = 0.0001
Porosity Vignettes × Absorption-0.06 (-0.16, 0.05)-0.02 (-0.17, 0.13)-0.04 (-0.19, 0.11)
p = 0.31p = 0.81p = 0.61
Country: Gh.0.01 (-0.21, 0.23)0.15 (-0.02, 0.31)0.10 (-0.13, 0.32)0.09 (-0.14, 0.32)
p = 0.92p = 0.08p = 0.41p = 0.46
Country: Th.-0.14 (-0.37, 0.08)-0.04 (-0.25, 0.16)-0.12 (-0.34, 0.10)-0.12 (-0.35, 0.11)
p = 0.21p = 0.69p = 0.31p = 0.31
Country: Ch.-0.17 (-0.42, 0.08)-0.41 (-0.60, -0.22)-0.25 (-0.55, 0.05)-0.25 (-0.55, 0.05)
p = 0.18p = 0.00003p = 0.11p = 0.10
Country: Va.0.70 (0.51, 0.90)0.80 (0.59, 1.00)0.68 (0.46, 0.90)0.65 (0.40, 0.89)
p = 0.00p = 0.00p = 0.00p = 0.0000004
Site: rural0.01 (-0.08, 0.10)0.04 (-0.05, 0.14)0.05 (-0.05, 0.14)0.04 (-0.08, 0.15)
p = 0.79p = 0.40p = 0.35p = 0.53
Religion: CC0.24 (0.15, 0.34)0.27 (0.18, 0.37)0.25 (0.16, 0.35)0.25 (0.16, 0.35)
p = 0.0000005p = 0.0000001p = 0.0000004p = 0.000001
Gender: male-0.06 (-0.15, 0.03)
p = 0.21
Age-0.09 (-0.19, 0.02)
p = 0.11
Education: at least HS-0.12 (-0.29, 0.06)
p = 0.20
Country: Gh. × Site: rural-0.26 (-0.43, -0.10)-0.24 (-0.40, -0.07)-0.27 (-0.44, -0.11)-0.23 (-0.41, -0.06)
p = 0.002p = 0.01p = 0.002p = 0.01
Country: Th. × Site: rural-0.03 (-0.24, 0.18)-0.03 (-0.24, 0.17)-0.03 (-0.23, 0.18)-0.01 (-0.22, 0.20)
p = 0.77p = 0.76p = 0.80p = 0.94
Country: Ch. × Site: rural0.43 (0.24, 0.62)0.49 (0.29, 0.69)0.48 (0.28, 0.68)0.44 (0.23, 0.65)
p = 0.00002p = 0.000002p = 0.000003p = 0.0001
Country: Va. × Site: rural0.06 (-0.13, 0.25)-0.03 (-0.24, 0.18)0.02 (-0.20, 0.24)-0.01 (-0.23, 0.22)
p = 0.53p = 0.77p = 0.86p = 0.95
Country: Gh. × Religion: CC-0.17 (-0.33, 0.001)-0.15 (-0.32, 0.02)-0.12 (-0.29, 0.05)-0.11 (-0.31, 0.09)
p = 0.06p = 0.08p = 0.17p = 0.31
Country: Th. × Religion: CC-0.18 (-0.39, 0.04)-0.11 (-0.32, 0.09)-0.16 (-0.37, 0.05)-0.16 (-0.38, 0.06)
p = 0.11p = 0.29p = 0.14p = 0.16
Country: Ch. × Religion: CC-0.16 (-0.35, 0.03)-0.12 (-0.31, 0.08)-0.10 (-0.29, 0.09)-0.13 (-0.32, 0.06)
p = 0.11p = 0.25p = 0.32p = 0.20
Country: Va. × Religion: CC0.21 (0.02, 0.40)0.12 (-0.09, 0.33)0.12 (-0.09, 0.33)0.14 (-0.07, 0.35)
p = 0.03p = 0.26p = 0.25p = 0.21
Site: rural × Religion: CC-0.07 (-0.17, 0.02)-0.13 (-0.23, -0.04)-0.11 (-0.21, -0.02)-0.13 (-0.23, -0.04)
p = 0.12p = 0.01p = 0.03p = 0.01
Country: Gh. × Porosity Vignettes-0.20 (-0.39, -0.001)-0.25 (-0.46, -0.04)-0.23 (-0.44, -0.03)
p = 0.05p = 0.02p = 0.03
Country: Th. × Porosity Vignettes-0.07 (-0.32, 0.17)-0.06 (-0.31, 0.19)-0.04 (-0.30, 0.21)
p = 0.56p = 0.65p = 0.74
Country: Ch. × Porosity Vignettes-0.03 (-0.27, 0.22)-0.08 (-0.36, 0.20)-0.05 (-0.34, 0.23)
p = 0.84p = 0.57p = 0.72
Country: Va. × Porosity Vignettes0.40 (0.06, 0.73)0.51 (0.10, 0.92)0.45 (0.03, 0.86)
p = 0.03p = 0.02p = 0.04
Country: Gh. × Absorption0.11 (-0.08, 0.30)0.16 (-0.05, 0.38)0.16 (-0.05, 0.38)
p = 0.26p = 0.15p = 0.14
Country: Th. × Absorption0.04 (-0.15, 0.22)0.09 (-0.11, 0.30)0.08 (-0.12, 0.29)
p = 0.69p = 0.38p = 0.43
Country: Ch. × Absorption0.04 (-0.16, 0.24)-0.03 (-0.34, 0.28)-0.05 (-0.36, 0.26)
p = 0.71p = 0.84p = 0.77
Country: Va. × Absorption-0.05 (-0.26, 0.15)-0.06 (-0.27, 0.15)-0.05 (-0.26, 0.16)
p = 0.61p = 0.60p = 0.66
Country: Gh. × Site: rural × Religion: CC-0.12 (-0.28, 0.04)-0.21 (-0.39, -0.03)-0.23 (-0.41, -0.05)-0.24 (-0.42, -0.06)
p = 0.16p = 0.03p = 0.02p = 0.02
Country: Th. × Site: rural × Religion: CC0.21 (-0.001, 0.42)0.30 (0.09, 0.51)0.25 (0.05, 0.46)0.25 (0.04, 0.46)
p = 0.06p = 0.01p = 0.02p = 0.03
Country: Ch. × Site: rural × Religion: CC-0.16 (-0.35, 0.03)-0.08 (-0.27, 0.12)-0.09 (-0.28, 0.10)-0.09 (-0.28, 0.10)
p = 0.11p = 0.44p = 0.38p = 0.37
Country: Va. × Site: rural × Religion: CC0.05 (-0.15, 0.24)-0.02 (-0.23, 0.19)0.05 (-0.16, 0.26)0.07 (-0.15, 0.28)
p = 0.65p = 0.86p = 0.64p = 0.55
Country: Gh. × Porosity Vignettes × Absorption-0.06 (-0.26, 0.14)-0.08 (-0.27, 0.12)
p = 0.55p = 0.46
Country: Th. × Porosity Vignettes × Absorption-0.17 (-0.41, 0.07)-0.18 (-0.42, 0.06)
p = 0.16p = 0.14
Country: Ch. × Porosity Vignettes × Absorption-0.03 (-0.33, 0.28)0.01 (-0.30, 0.31)
p = 0.87p = 0.97
Country: Va. × Porosity Vignettes × Absorption0.31 (-0.17, 0.79)0.26 (-0.21, 0.74)
p = 0.21p = 0.29
Constant-0.01 (-0.12, 0.09)-0.01 (-0.12, 0.10)-0.01 (-0.11, 0.10)0.03 (-0.08, 0.14)0.01 (-0.08, 0.11)0.04 (-0.07, 0.16)0.09 (-0.05, 0.23)
p = 0.79p = 0.82p = 0.93p = 0.62p = 0.79p = 0.47p = 0.20
Observations318301297318301297293
R20.130.040.140.460.450.500.52
Adjusted R20.130.030.130.420.400.440.45
Residual Std. Error0.94 (df = 316)0.97 (df = 299)0.92 (df = 293)0.77 (df = 293)0.76 (df = 276)0.74 (df = 262)0.74 (df = 255)
F Statistic48.40*** (df = 1; 316)11.87*** (df = 1; 299)16.20*** (df = 3; 293)10.38*** (df = 24; 293)9.39*** (df = 24; 276)7.73*** (df = 34; 262)7.43*** (df = 37; 255)

Table S21. Study 1, Spiritual Events: relationships with Porosity Vignettes and Absorption (sample as random effect)

r11_spev <- lmer(`Spiritual Events` ~ 1 
                 + (1 | Country/Site/Religion),
                 control = lmerControl(optimizer = "bobyqa"),
                 data = d1_abs_omit)
boundary (singular) fit: see ?isSingular
r12_spev <- lmer(`Spiritual Events` ~ `Porosity Vignettes` 
                 # + (1 + `Porosity Vignettes` | Country/Site/Religion),
                 + (1 | Country/Site/Religion),
                 control = lmerControl(optimizer = "bobyqa"),
                 data = d1_abs_omit)
boundary (singular) fit: see ?isSingular
r13_spev <- lmer(`Spiritual Events` ~ Absorption
                 # + (1 + Absorption | Country/Site/Religion),
                 + (1 | Country/Site/Religion),
                 control = lmerControl(optimizer = "bobyqa"),
                 data = d1_abs_omit)
boundary (singular) fit: see ?isSingular
r14_spev <- lmer(`Spiritual Events` ~ `Porosity Vignettes` * Absorption 
                 # + (1 + `Porosity Vignettes` * Absorption | Country/Site/Religion),
                 # + (1 + `Porosity Vignettes` + Absorption | Country/Site/Religion),
                 + (1 | Country/Site/Religion),
                 control = lmerControl(optimizer = "bobyqa"),
                 data = d1_abs_omit)

r15_spev <- lmer(`Spiritual Events` ~ `Porosity Vignettes` * Absorption
                 + Gender + Age + Education
                 # + (1 + `Porosity Vignettes` * Absorption | Country/Site/Religion),
                 # + (1 + `Porosity Vignettes` + Absorption | Country/Site/Religion),
                 + (1 | Country/Site/Religion),
                 control = lmerControl(optimizer = "bobyqa"),
                 data = d1_abs_omit)
boundary (singular) fit: see ?isSingular
htmltools::includeHTML("./stargazer_tables/table_s21.html")
Spiritual Events
(1)(2)(3)(4)(5)
Porosity Vignettes0.29 (0.18, 0.40)0.22 (0.11, 0.34)0.21 (0.10, 0.32)
p = 0.0000002p = 0.0001p = 0.0002
Absorption0.25 (0.16, 0.34)0.20 (0.11, 0.30)0.20 (0.11, 0.29)
p = 0.0000002p = 0.00002p = 0.00003
Porosity Vignettes × Absorption-0.04 (-0.13, 0.05)-0.06 (-0.15, 0.03)
p = 0.43p = 0.23
Gender: male-0.05 (-0.14, 0.04)
p = 0.30
Age-0.09 (-0.19, 0.01)
p = 0.09
Education: at least HS-0.18 (-0.33, -0.03)
p = 0.03
Constant0.01 (-0.45, 0.48)0.01 (-0.38, 0.40)0.01 (-0.44, 0.46)0.02 (-0.37, 0.42)0.09 (-0.26, 0.45)
p = 0.96p = 0.96p = 0.96p = 0.92p = 0.61
Observations334318301297293
Log Likelihood-421.69-392.09-369.17-361.22-357.15
Akaike Inf. Crit.853.38796.18750.33738.44736.29
Bayesian Inf. Crit.872.44818.75772.58767.99776.77
rsq_table_fun(list(r11_spev, r12_spev, r13_spev, r14_spev, r15_spev))
R-squared Type (1) (2) (3) (4) (5)
Marginal 0.00 0.08 0.06 0.11 0.14
Conditional 0.39 0.41 0.46 0.45 0.45

Study 3

d3_abs_omit <- d3_byq %>%
  select(subj, ctry, question, response) %>%
  filter(question %in% c("exwl_01", "exwl_02", "exwl_03", "exwl_04", "exwl_05", "exwl_06",
                         "exwl_07", "exwl_08", "exwl_09", "exwl_10", "exwl_11", "exwl_12",
                         "exwl_13", "exwl_14", "exwl_15", "exwl_16", "exwl_17", "exwl_18",
                         "exwl_19", "exwl_20", "exwl_21", "exwl_22", "exwl_23", "exwl_24",
                         "exwl_25", "exwl_26", "exwl_27", "exwl_28", "exwl_29", "exwl_30",
                         "exwl_31", "exwl_32", "exwl_33", "exwl_34")) %>%
  filter(question != "exwl_14", question != "exwl_31") %>%
  group_by(subj, ctry) %>%
  summarise(Absorption = mean(response, na.rm = T)) %>%
  ungroup() %>%
  group_by(ctry) %>%
  mutate(Absorption = scale(Absorption)) %>%
  ungroup() %>%
  distinct(subj, Absorption) %>%
  rename(subject_id = subj) %>%
  right_join(d3_fortab %>% select(-Absorption)) %>%
  filter(subject_id %in% d3_fortab$subject_id)
`summarise()` has grouped output by 'subj'. You can override using the `.groups` argument.
Joining, by = "subject_id"

Table S32. Study 3, Spiritual Events and Daily Spiritual Experience: relationships with Absorption (sample as fixed effect)

r03_spev <- lm(`Spiritual Events` ~ `Absorption`,
               data = d3_abs_omit)

r04_spev <- lm(`Spiritual Events` ~ Country * `Absorption`,
               data = d3_abs_omit)

r05_spev <- lm(`Spiritual Events` ~ Country * `Absorption`
               + Gender + Age,  
               data = d3_abs_omit)
r03_dse <- lm(`Daily Spiritual Experiences` ~ `Absorption`,
              data = d3_abs_omit)

r04_dse <- lm(`Daily Spiritual Experiences` ~ Country * `Absorption`,
              data = d3_abs_omit)

r05_dse <- lm(`Daily Spiritual Experiences` ~ Country * `Absorption`
              + Gender + Age,  
              data = d3_abs_omit)
table_s32 <- stargazer(r03_spev, r04_spev, r05_spev, r03_dse, r04_dse, r05_dse, 
                       type = "html",
                       ci = T,
                       report = c("vcsp"),
                       digits = 2,
                       digits.extra = 1000,
                       omit.table.layout = "n",
                       order = c("^Absorption$"),
                       # dep.var.labels.include = F,
                       dep.var.caption = "",
                       single.row = T)
cat(sub_interact_fun(table_s32), 
    file = "./stargazer_tables/table_s32.html")
htmltools::includeHTML("./stargazer_tables/table_s32.html")
Spiritual EventsDaily Spiritual Experiences
(1)(2)(3)(4)(5)(6)
Absorption0.36 (0.28, 0.44)0.36 (0.30, 0.42)0.36 (0.30, 0.42)0.22 (0.14, 0.31)0.22 (0.16, 0.28)0.22 (0.16, 0.28)
p = 0.00p = 0.00p = 0.00p = 0.0000004p = 0.00p = 0.00
Country: Gh.0.89 (0.76, 1.01)0.90 (0.77, 1.02)0.87 (0.75, 0.99)0.89 (0.77, 1.01)
p = 0.00p = 0.00p = 0.00p = 0.00
Country: Th.-0.50 (-0.62, -0.39)-0.49 (-0.61, -0.36)-0.18 (-0.29, -0.07)-0.17 (-0.29, -0.05)
p = 0.00p = 0.00p = 0.002p = 0.005
Country: Ch.-0.67 (-0.79, -0.55)-0.67 (-0.80, -0.55)-1.13 (-1.25, -1.02)-1.14 (-1.25, -1.02)
p = 0.00p = 0.00p = 0.00p = 0.00
Country: Va.0.59 (0.47, 0.71)0.53 (0.39, 0.67)0.66 (0.54, 0.77)0.58 (0.45, 0.72)
p = 0.00p = 0.00p = 0.00p = 0.00
Gender: male-0.02 (-0.09, 0.04)-0.06 (-0.12, 0.001)
p = 0.50p = 0.06
Age0.06 (-0.01, 0.14)0.09 (0.02, 0.16)
p = 0.10p = 0.02
Country: Gh. × Absorption0.23 (0.11, 0.36)0.23 (0.11, 0.36)0.01 (-0.11, 0.13)0.005 (-0.11, 0.12)
p = 0.0003p = 0.0003p = 0.92p = 0.94
Country: Th. × Absorption-0.16 (-0.27, -0.04)-0.16 (-0.28, -0.04)0.08 (-0.03, 0.20)0.08 (-0.04, 0.19)
p = 0.01p = 0.01p = 0.16p = 0.19
Country: Ch. × Absorption-0.13 (-0.25, -0.01)-0.12 (-0.24, -0.0004)-0.03 (-0.14, 0.09)-0.02 (-0.14, 0.10)
p = 0.04p = 0.05p = 0.65p = 0.73
Country: Va. × Absorption0.06 (-0.06, 0.18)0.05 (-0.07, 0.17)-0.09 (-0.21, 0.02)-0.11 (-0.23, 0.01)
p = 0.34p = 0.43p = 0.12p = 0.08
Constant0.00 (-0.08, 0.08)0.03 (-0.03, 0.09)0.02 (-0.04, 0.08)0.00 (-0.08, 0.08)0.02 (-0.04, 0.08)0.01 (-0.05, 0.07)
p = 1.00p = 0.41p = 0.47p = 1.00p = 0.50p = 0.71
Observations519519513519519513
R20.130.520.530.050.550.56
Adjusted R20.130.520.520.050.540.55
Residual Std. Error0.94 (df = 517)0.70 (df = 509)0.70 (df = 501)0.98 (df = 517)0.67 (df = 509)0.67 (df = 501)
F Statistic75.27*** (df = 1; 517)62.17*** (df = 9; 509)50.68*** (df = 11; 501)26.75*** (df = 1; 517)69.85*** (df = 9; 509)58.40*** (df = 11; 501)

Table S33. Study 3, Spiritual Events and Daily Spiritual Experience: relationships with Absorption (sample as random effect)

r06_spev <- lmer(`Spiritual Events` ~ 1 
                 + (1 | Country),
                 control = lmerControl(optimizer = "bobyqa"),
                 data = d3_abs_omit)

r07_spev <- lmer(`Spiritual Events` ~ `Absorption` 
                 # + (1 + `Absorption` | Country), # perfectly correlated ranfx
                 + (1 | Country),
                 control = lmerControl(optimizer = "bobyqa"),
                 data = d3_abs_omit)

r08_spev <- lmer(`Spiritual Events` ~ `Absorption`
                 + Gender + Age 
                 # + (1 + `Absorption` + Gender + Age | Country), # perfectly correlated ranfx
                 # + (1 + `Absorption` | Country), # perfectly correlated ranfx
                 + (1 | Country),
                 control = lmerControl(optimizer = "bobyqa"),
                 data = d3_abs_omit)
r06_dse <- lmer(`Daily Spiritual Experiences` ~ 1 
                + (1 | Country),
                control = lmerControl(optimizer = "bobyqa"),
                data = d3_abs_omit)

r07_dse <- lmer(`Daily Spiritual Experiences` ~ `Absorption` 
                # + (1 + `Absorption` | Country), # perfectly correlated ranfx
                + (1 | Country),
                control = lmerControl(optimizer = "bobyqa"),
                data = d3_abs_omit)

r08_dse <- lmer(`Daily Spiritual Experiences` ~ `Absorption`
                + Gender + Age 
                # + (1 + `Absorption` + Gender + Age | Country), 
                # + (1 + `Absorption` | Country),
                + (1 | Country), # use this for comparsion with spev
                control = lmerControl(optimizer = "bobyqa"),
                data = d3_abs_omit)
table_s33 <- stargazer(r06_spev, r07_spev, r08_spev, r06_dse, r07_dse, r08_dse,
                       type = "html",
                       ci = T,
                       report = c("vcsp"),
                       digits = 2,
                       digits.extra = 0800,
                       omit.table.layout = "n",
                       order = c("^`Absorption`$"),
                       # dep.var.labels.include = F,
                       dep.var.caption = "",
                       single.row = T)
cat(sub_interact_fun(table_s33), 
    file = "./stargazer_tables/table_s33.html")
htmltools::includeHTML("./stargazer_tables/table_s33.html")
Spiritual EventsDaily Spiritual Experiences
(1)(2)(3)(4)(5)(6)
Absorption0.36 (0.30, 0.42)0.36 (0.29, 0.42)0.22 (0.16, 0.28)0.22 (0.16, 0.28)
p = 0.00p = 0.00p = 0.00p = 0.00
Gender: male-0.02 (-0.09, 0.05)-0.07 (-0.13, -0.004)
p = 0.55p = 0.04
Age0.07 (-0.003, 0.15)0.08 (0.01, 0.15)
p = 0.07p = 0.03
Constant0.03 (-0.58, 0.63)0.03 (-0.58, 0.63)0.02 (-0.57, 0.62)0.02 (-0.68, 0.72)0.02 (-0.68, 0.72)0.01 (-0.68, 0.70)
p = 0.94p = 0.94p = 0.95p = 0.96p = 0.96p = 0.98
Observations519519513519519513
Log Likelihood-626.20-570.08-567.78-570.44-546.42-541.27
Akaike Inf. Crit.1,258.401,148.161,147.561,146.881,100.841,094.54
Bayesian Inf. Crit.1,271.151,165.161,173.001,159.631,117.851,119.98
rsq_table_fun(list(r06_spev, r07_spev, r08_spev, r06_dse, r07_dse, r08_dse))
R-squared Type (1) (2) (3) (4) (5) (6)
Marginal 0.00 0.12 0.12 0.00 0.04 0.05
Conditional 0.43 0.55 0.54 0.56 0.60 0.60

Table S34. Study 3, Spiritual Events: relationships with Absorption by country

r09_us_spev <- lm(`Spiritual Events` ~ `Absorption`,
                  data = d3_abs_omit %>% 
                    filter(Country == "US") %>%
                    mutate_at(vars(`Spiritual Events`, `Absorption`),
                              ~ scale(.)))

r09_gh_spev <- lm(`Spiritual Events` ~ `Absorption`,
                  data = d3_abs_omit %>% 
                    filter(Country == "Ghana") %>%
                    mutate_at(vars(`Spiritual Events`, `Absorption`),
                              ~ scale(.)))

r09_th_spev <- lm(`Spiritual Events` ~ `Absorption`,
                  data = d3_abs_omit %>% 
                    filter(Country == "Thailand") %>%
                    mutate_at(vars(`Spiritual Events`, `Absorption`),
                              ~ scale(.)))

r09_ch_spev <- lm(`Spiritual Events` ~ `Absorption`,
                  data = d3_abs_omit %>% 
                    filter(Country == "China") %>%
                    mutate_at(vars(`Spiritual Events`, `Absorption`),
                              ~ scale(.)))

r09_vt_spev <- lm(`Spiritual Events` ~ `Absorption`,
                  data = d3_abs_omit %>% 
                    filter(Country == "Vanuatu") %>%
                    mutate_at(vars(`Spiritual Events`, `Absorption`),
                              ~ scale(.)))
table_s34 <- stargazer(r09_us_spev, r09_gh_spev, r09_th_spev, r09_ch_spev, r09_vt_spev, 
                       type = "html",
                       ci = T,
                       report = c("vcsp"),
                       digits = 2,
                       digits.extra = 1000,
                       omit.table.layout = "n",
                       order = c("^`Absorption`$"),
                       # dep.var.labels.include = F,
                       dep.var.caption = "",
                       single.row = T)
cat(sub_interact_fun(table_s34), 
    file = "./stargazer_tables/table_s34.html")
htmltools::includeHTML("./stargazer_tables/table_s34.html")
Spiritual Events
(1)(2)(3)(4)(5)
Absorption0.39 (0.21, 0.57)0.56 (0.40, 0.73)0.43 (0.27, 0.60)0.48 (0.32, 0.65)0.48 (0.31, 0.65)
p = 0.00004p = 0.00p = 0.000002p = 0.0000002p = 0.0000005
Constant-0.00 (-0.18, 0.18)-0.00 (-0.17, 0.17)0.00 (-0.17, 0.17)-0.00 (-0.17, 0.17)0.00 (-0.17, 0.17)
p = 1.00p = 1.00p = 1.00p = 1.00p = 1.00
Observations10697112104100
R20.150.320.190.240.23
Adjusted R20.140.310.180.230.22
Residual Std. Error0.93 (df = 104)0.83 (df = 95)0.91 (df = 110)0.88 (df = 102)0.88 (df = 98)
F Statistic18.57*** (df = 1; 104)43.73*** (df = 1; 95)25.50*** (df = 1; 110)31.36*** (df = 1; 102)29.24*** (df = 1; 98)

Table S35. Study 3, Daily Spiritual Experience: relationships with Absorption by country

r09_us_dse <- lm(`Daily Spiritual Experiences` ~ `Absorption`,
                 data = d3_abs_omit %>% 
                   filter(Country == "US") %>%
                   mutate_at(vars(`Daily Spiritual Experiences`, `Absorption`),
                             ~ scale(.)))

r09_gh_dse <- lm(`Daily Spiritual Experiences` ~ `Absorption`,
                 data = d3_abs_omit %>% 
                   filter(Country == "Ghana") %>%
                   mutate_at(vars(`Daily Spiritual Experiences`, `Absorption`),
                             ~ scale(.)))

r09_th_dse <- lm(`Daily Spiritual Experiences` ~ `Absorption`,
                 data = d3_abs_omit %>% 
                   filter(Country == "Thailand") %>%
                   mutate_at(vars(`Daily Spiritual Experiences`, `Absorption`),
                             ~ scale(.)))

r09_ch_dse <- lm(`Daily Spiritual Experiences` ~ `Absorption`,
                 data = d3_abs_omit %>% 
                   filter(Country == "China") %>%
                   mutate_at(vars(`Daily Spiritual Experiences`, `Absorption`),
                             ~ scale(.)))

r09_vt_dse <- lm(`Daily Spiritual Experiences` ~ `Absorption`,
                 data = d3_abs_omit %>% 
                   filter(Country == "Vanuatu") %>%
                   mutate_at(vars(`Daily Spiritual Experiences`, `Absorption`),
                             ~ scale(.)))
table_s35 <- stargazer(r09_us_dse, r09_gh_dse, r09_th_dse, r09_ch_dse, r09_vt_dse, 
                       type = "html",
                       ci = T,
                       report = c("vcsp"),
                       digits = 2,
                       digits.extra = 1000,
                       omit.table.layout = "n",
                       order = c("^`Absorption`$"),
                       # dep.var.labels.include = F,
                       dep.var.caption = "",
                       single.row = T)
cat(sub_interact_fun(table_s35), 
    file = "./stargazer_tables/table_s35.html")
htmltools::includeHTML("./stargazer_tables/table_s35.html")
Daily Spiritual Experiences
(1)(2)(3)(4)(5)
Absorption0.24 (0.05, 0.43)0.35 (0.16, 0.53)0.54 (0.38, 0.70)0.39 (0.21, 0.57)0.20 (0.004, 0.39)
p = 0.02p = 0.001p = 0.00p = 0.00005p = 0.05
Constant0.00 (-0.19, 0.19)0.00 (-0.19, 0.19)-0.00 (-0.16, 0.16)0.00 (-0.18, 0.18)-0.00 (-0.19, 0.19)
p = 1.00p = 1.00p = 1.00p = 1.00p = 1.00
Observations10697112104100
R20.060.120.290.150.04
Adjusted R20.050.110.280.140.03
Residual Std. Error0.98 (df = 104)0.94 (df = 95)0.85 (df = 110)0.93 (df = 102)0.99 (df = 98)
F Statistic6.36** (df = 1; 104)12.87*** (df = 1; 95)44.79*** (df = 1; 110)18.23*** (df = 1; 102)4.00** (df = 1; 98)

Study 4

Relationships: Spiritual presence events

d4_abs_omit <- d4_byq %>%
  select(p7_subj, p7_ctry, contains("_abs_")) %>%
  select(-ends_with("cat"), -ends_with("total"), -contains("_check")) %>%
  select(-p7_abs_sense.presc, -p7_abs_not.physical) %>%
  gather(question, response, -p7_subj, -p7_ctry) %>%
  group_by(p7_subj, p7_ctry) %>%
  summarise(Absorption = mean(response, na.rm = T)) %>%
  ungroup() %>%
  group_by(p7_ctry) %>%
  mutate(Absorption = scale(Absorption)) %>%
  ungroup() %>%
  distinct(p7_subj, Absorption) %>%
  rename(subject_id = p7_subj) %>%
  right_join(d4_fortab %>% select(-Absorption)) %>%
  filter(subject_id %in% d4_fortab$subject_id)
`summarise()` has grouped output by 'p7_subj'. You can override using the `.groups` argument.
Joining, by = "subject_id"
# longform dataframe
d4_fortab_long_spirit <- d4_abs_omit %>% 
  gather(Scale, Score, 
         c(`Spiritual Events`, `Daily Spiritual Experience`)) %>%
  mutate(Scale = factor(Scale))
# set contrasts
contrasts(d4_fortab_long_spirit$Country) <- cbind(": Gh." = c(-1, 1, 0, 0, 0),
                                                  ": Th." = c(-1, 0, 1, 0, 0),
                                                  ": Ch." = c(-1, 0, 0, 1, 0),
                                                  ": Va." = c(-1, 0, 0, 0, 1))
contrasts(d4_fortab_long_spirit$Gender) <- cbind(": male" = c(-1, 1))
contrasts(d4_fortab_long_spirit$Scale) <- cbind(": Spiritual Events" = c(-1, 1))

Table S40. Study 4, measures of spiritual presence events: relationships with Porosity Vignettes and Absorption (sample as fixed effect)

r03_spirit <- lmer(Score ~ Scale * `Porosity Vignettes`
                   + (1 | subject_id),
                   control = lmerControl(optimizer = "bobyqa"),
                   data = d4_fortab_long_spirit)

r04_spirit <- lmer(Score ~ Scale * `Porosity Scale`
                   + (1 | subject_id),
                   control = lmerControl(optimizer = "bobyqa"),
                   data = d4_fortab_long_spirit)

r05_spirit <- lmer(Score ~ Scale * Absorption
                   + (1 | subject_id),
                   control = lmerControl(optimizer = "bobyqa"),
                   data = d4_fortab_long_spirit)

r06_spirit <- lmer(Score ~ Scale * Country * `Porosity Vignettes`
                   + (1 | subject_id),
                   control = lmerControl(optimizer = "bobyqa"),
                   data = d4_fortab_long_spirit)

r07_spirit <- lmer(Score ~ Scale * Country * `Porosity Scale`
                   + (1 | subject_id),
                   control = lmerControl(optimizer = "bobyqa"),
                   data = d4_fortab_long_spirit)

r08_spirit <- lmer(Score ~ Scale * Country * Absorption
                   + (1 | subject_id),
                   control = lmerControl(optimizer = "bobyqa"),
                   data = d4_fortab_long_spirit)

r09_spirit <- lmer(Score ~ Scale * Country * `Porosity Vignettes` * Absorption
                   + (1 | subject_id),
                   control = lmerControl(optimizer = "bobyqa"),
                   data = d4_fortab_long_spirit)

r10_spirit <- lmer(Score ~ Scale * Country * `Porosity Scale` * Absorption
                   + (1 | subject_id),
                   control = lmerControl(optimizer = "bobyqa"),
                   data = d4_fortab_long_spirit)

r11_spirit <- lmer(Score ~ Scale * Country * `Porosity Vignettes` * Absorption
                   + Gender + Age
                   + (1 | subject_id),
                   control = lmerControl(optimizer = "bobyqa"),
                   data = d4_fortab_long_spirit)

r12_spirit <- lmer(Score ~ Scale * Country * `Porosity Scale` * Absorption
                   + Gender + Age
                   + (1 | subject_id),
                   control = lmerControl(optimizer = "bobyqa"),
                   data = d4_fortab_long_spirit)
table_s40 <- stargazer(r03_spirit, #r04_spirit, 
                       r05_spirit, r06_spirit, #r07_spirit, 
                       r08_spirit, r09_spirit, #r10_spirit,
                       r11_spirit, #r12_spirit, 
                       type = "html",
                       ci = T,
                       report = c("vcsp"),
                       digits = 2,
                       digits.extra = 1000,
                       omit.table.layout = "n",
                       order = c("^`Porosity Vignettes`$",
                                 "^Absorption$",
                                 "^`Porosity Vignettes`:Absorption$"),
                       # dep.var.labels.include = F,
                       dep.var.caption = "",
                       single.row = T) 
cat(sub_interact_fun(table_s40), 
    file = "./stargazer_tables/table_s40.html")
htmltools::includeHTML("./stargazer_tables/table_s40.html")
Score
(1)(2)(3)(4)(5)(6)
Porosity Vignettes0.53 (0.47, 0.60)0.27 (0.20, 0.33)0.23 (0.16, 0.29)0.23 (0.16, 0.30)
p = 0.00p = 0.00p = 0.00p = 0.00
Absorption0.20 (0.12, 0.28)0.20 (0.14, 0.26)0.18 (0.11, 0.24)0.17 (0.11, 0.24)
p = 0.0000004p = 0.00p = 0.0000004p = 0.000001
Porosity Vignettes × Absorption-0.01 (-0.07, 0.06)0.003 (-0.07, 0.07)
p = 0.87p = 0.94
Scale: Spiritual Events-0.001 (-0.04, 0.03)-0.001 (-0.04, 0.03)-0.02 (-0.06, 0.02)-0.001 (-0.03, 0.03)-0.02 (-0.06, 0.02)-0.02 (-0.06, 0.03)
p = 0.94p = 0.94p = 0.38p = 0.98p = 0.42p = 0.43
Country: Gh.0.69 (0.56, 0.82)0.83 (0.72, 0.94)0.73 (0.60, 0.86)0.71 (0.58, 0.84)
p = 0.00p = 0.00p = 0.00p = 0.00
Country: Th.-0.29 (-0.40, -0.17)-0.29 (-0.39, -0.18)-0.32 (-0.43, -0.20)-0.32 (-0.43, -0.20)
p = 0.000001p = 0.0000002p = 0.00000004p = 0.0000002
Country: Ch.-0.68 (-0.83, -0.52)-0.86 (-0.97, -0.75)-0.70 (-0.85, -0.55)-0.67 (-0.83, -0.52)
p = 0.00p = 0.00p = 0.00p = 0.00
Country: Va.0.50 (0.36, 0.65)0.62 (0.51, 0.74)0.53 (0.38, 0.67)0.49 (0.34, 0.65)
p = 0.00p = 0.00p = 0.00p = 0.00
Scale: Spiritual Events × Country: Gh.-0.06 (-0.14, 0.02)-0.02 (-0.08, 0.05)-0.05 (-0.13, 0.03)-0.05 (-0.13, 0.03)
p = 0.12p = 0.65p = 0.22p = 0.24
Scale: Spiritual Events × Country: Th.-0.09 (-0.16, -0.02)-0.12 (-0.18, -0.05)-0.10 (-0.18, -0.03)-0.11 (-0.18, -0.03)
p = 0.02p = 0.001p = 0.005p = 0.01
Scale: Spiritual Events × Country: Ch.0.16 (0.06, 0.26)0.20 (0.13, 0.27)0.16 (0.06, 0.26)0.16 (0.06, 0.26)
p = 0.002p = 0.00p = 0.002p = 0.003
Scale: Spiritual Events × Country: Va.-0.13 (-0.22, -0.03)-0.14 (-0.21, -0.07)-0.10 (-0.19, -0.01)-0.10 (-0.20, -0.003)
p = 0.01p = 0.0002p = 0.04p = 0.05
Scale: Spiritual Events × Porosity Vignettes0.01 (-0.03, 0.04)0.04 (-0.003, 0.08)0.03 (-0.01, 0.07)0.03 (-0.01, 0.08)
p = 0.75p = 0.07p = 0.20p = 0.17
Gender: male-0.02 (-0.07, 0.04)
p = 0.58
Age0.06 (-0.01, 0.12)
p = 0.08
Scale: Spiritual Events × Absorption0.04 (0.01, 0.08)0.04 (0.01, 0.08)0.05 (0.01, 0.09)0.05 (0.004, 0.09)
p = 0.02p = 0.02p = 0.03p = 0.04
Country: Gh. × Porosity Vignettes-0.11 (-0.23, 0.002)-0.11 (-0.23, 0.01)-0.12 (-0.23, 0.002)
p = 0.06p = 0.07p = 0.06
Country: Th. × Porosity Vignettes-0.04 (-0.17, 0.08)-0.07 (-0.19, 0.06)-0.08 (-0.21, 0.04)
p = 0.49p = 0.28p = 0.20
Country: Ch. × Porosity Vignettes0.07 (-0.10, 0.23)0.08 (-0.08, 0.25)0.06 (-0.10, 0.23)
p = 0.45p = 0.33p = 0.45
Country: Va. × Porosity Vignettes-0.15 (-0.30, -0.0001)-0.14 (-0.30, 0.01)-0.14 (-0.30, 0.01)
p = 0.05p = 0.07p = 0.08
Scale: Spiritual Events × Country: Gh. × Porosity Vignettes0.07 (-0.003, 0.14)0.07 (-0.002, 0.15)0.07 (-0.01, 0.15)
p = 0.06p = 0.06p = 0.08
Scale: Spiritual Events × Country: Th. × Porosity Vignettes-0.01 (-0.09, 0.07)0.003 (-0.08, 0.08)0.0004 (-0.08, 0.08)
p = 0.85p = 0.94p = 1.00
Scale: Spiritual Events × Country: Ch. × Porosity Vignettes-0.12 (-0.22, -0.01)-0.11 (-0.22, -0.01)-0.12 (-0.22, -0.01)
p = 0.04p = 0.04p = 0.04
Scale: Spiritual Events × Country: Va. × Porosity Vignettes-0.03 (-0.13, 0.07)-0.04 (-0.14, 0.05)-0.05 (-0.15, 0.06)
p = 0.54p = 0.37p = 0.38
Country: Gh. × Absorption-0.04 (-0.16, 0.07)-0.01 (-0.13, 0.11)-0.001 (-0.12, 0.12)
p = 0.45p = 0.91p = 0.99
Country: Th. × Absorption0.03 (-0.07, 0.14)0.03 (-0.09, 0.14)0.03 (-0.08, 0.14)
p = 0.54p = 0.64p = 0.61
Country: Ch. × Absorption-0.03 (-0.15, 0.08)0.04 (-0.13, 0.21)0.04 (-0.13, 0.21)
p = 0.57p = 0.62p = 0.64
Country: Va. × Absorption-0.07 (-0.18, 0.05)-0.01 (-0.16, 0.13)-0.01 (-0.16, 0.14)
p = 0.26p = 0.86p = 0.90
Scale: Spiritual Events × Country: Gh. × Absorption0.01 (-0.06, 0.08)-0.01 (-0.08, 0.07)-0.01 (-0.09, 0.07)
p = 0.80p = 0.88p = 0.88
Scale: Spiritual Events × Country: Th. × Absorption-0.04 (-0.11, 0.02)-0.05 (-0.12, 0.02)-0.05 (-0.12, 0.02)
p = 0.20p = 0.18p = 0.19
Scale: Spiritual Events × Country: Ch. × Absorption-0.01 (-0.08, 0.06)-0.02 (-0.13, 0.09)-0.02 (-0.14, 0.09)
p = 0.86p = 0.68p = 0.68
Scale: Spiritual Events × Country: Va. × Absorption0.01 (-0.07, 0.08)0.08 (-0.02, 0.17)0.07 (-0.03, 0.18)
p = 0.88p = 0.11p = 0.15
Scale: Spiritual Events × Porosity Vignettes × Absorption-0.01 (-0.06, 0.03)-0.01 (-0.06, 0.03)
p = 0.56p = 0.65
Country: Gh. × Porosity Vignettes × Absorption-0.10 (-0.21, 0.01)-0.10 (-0.22, 0.01)
p = 0.09p = 0.08
Country: Th. × Porosity Vignettes × Absorption0.06 (-0.06, 0.19)0.05 (-0.07, 0.18)
p = 0.33p = 0.42
Country: Ch. × Porosity Vignettes × Absorption0.09 (-0.08, 0.27)0.07 (-0.10, 0.25)
p = 0.30p = 0.42
Country: Va. × Porosity Vignettes × Absorption-0.05 (-0.19, 0.09)-0.06 (-0.21, 0.08)
p = 0.49p = 0.40
Scale: Spiritual Events × Country: Gh. × Porosity Vignettes × Absorption-0.02 (-0.10, 0.05)-0.03 (-0.10, 0.05)
p = 0.54p = 0.50
Scale: Spiritual Events × Country: Th. × Porosity Vignettes × Absorption0.07 (-0.01, 0.15)0.07 (-0.01, 0.15)
p = 0.08p = 0.09
Scale: Spiritual Events × Country: Ch. × Porosity Vignettes × Absorption-0.01 (-0.12, 0.11)-0.01 (-0.13, 0.11)
p = 0.90p = 0.88
Scale: Spiritual Events × Country: Va. × Porosity Vignettes × Absorption-0.10 (-0.19, -0.01)-0.10 (-0.19, -0.00002)
p = 0.04p = 0.05
Constant-0.001 (-0.07, 0.06)-0.001 (-0.08, 0.08)0.06 (-0.002, 0.13)0.02 (-0.04, 0.07)0.07 (0.002, 0.13)0.06 (-0.01, 0.13)
p = 0.97p = 0.98p = 0.06p = 0.56p = 0.05p = 0.10
Observations1,0081,0081,0081,0081,008978
Log Likelihood-1,186.34-1,275.26-1,082.26-1,113.46-1,105.26-1,083.77
Akaike Inf. Crit.2,384.672,562.522,208.532,270.932,294.512,255.54
Bayesian Inf. Crit.2,414.162,592.012,316.672,379.082,500.972,470.50
rsq_table_fun(list(r03_spirit, #r04_spirit, 
                   r05_spirit, r06_spirit, #r07_spirit, 
                   r08_spirit, r09_spirit, #r10_spirit,
                   r11_spirit #, r12_spirit 
))
R-squared Type (1) (2) (3) (4) (5) (6)
Marginal 0.28 0.04 0.49 0.45 0.51 0.51
Conditional 0.67 0.67 0.71 0.70 0.71 0.71

Table S41. Study 4, measures of spiritual presence events: relationships with Porosity Scale and Absorption (sample as fixed effect)

table_s41 <- stargazer(#r03_spirit, 
  r04_spirit, r05_spirit, #r06_spirit,
  r07_spirit, r08_spirit, #r09_spirit, 
  r10_spirit, #r11_spirit, 
  r12_spirit, 
  type = "html",
  ci = T,
  report = c("vcsp"),
  digits = 2,
  digits.extra = 1000,
  omit.table.layout = "n",
  order = c("^`Porosity Scale`$",
            "^Absorption$",
            "^`Porosity Scale`:Absorption$"),
  # dep.var.labels.include = F,
  dep.var.caption = "",
  single.row = T) 
cat(sub_interact_fun(table_s41), 
    file = "./stargazer_tables/table_s41.html")
htmltools::includeHTML("./stargazer_tables/table_s41.html")
Score
(1)(2)(3)(4)(5)(6)
Porosity Scale0.62 (0.56, 0.68)0.36 (0.29, 0.44)0.32 (0.25, 0.39)0.33 (0.25, 0.40)
p = 0.00p = 0.00p = 0.00p = 0.00
Absorption0.20 (0.12, 0.28)0.20 (0.14, 0.26)0.15 (0.07, 0.22)0.14 (0.07, 0.22)
p = 0.0000004p = 0.00p = 0.0001p = 0.0002
Porosity Scale × Absorption0.02 (-0.05, 0.09)0.02 (-0.04, 0.09)
p = 0.57p = 0.49
Scale: Spiritual Events-0.001 (-0.04, 0.03)-0.001 (-0.04, 0.03)-0.002 (-0.05, 0.05)-0.001 (-0.03, 0.03)-0.01 (-0.06, 0.04)-0.004 (-0.06, 0.05)
p = 0.94p = 0.94p = 0.94p = 0.98p = 0.81p = 0.88
Country: Gh.0.62 (0.49, 0.75)0.83 (0.72, 0.94)0.65 (0.52, 0.78)0.62 (0.49, 0.76)
p = 0.00p = 0.00p = 0.00p = 0.00
Country: Th.-0.24 (-0.36, -0.13)-0.29 (-0.39, -0.18)-0.26 (-0.37, -0.14)-0.25 (-0.37, -0.12)
p = 0.00004p = 0.0000002p = 0.00002p = 0.0001
Country: Ch.-0.57 (-0.73, -0.41)-0.86 (-0.97, -0.75)-0.63 (-0.80, -0.46)-0.61 (-0.78, -0.44)
p = 0.00p = 0.00p = 0.00p = 0.00
Country: Va.0.38 (0.19, 0.57)0.62 (0.51, 0.74)0.42 (0.22, 0.61)0.38 (0.18, 0.58)
p = 0.0002p = 0.00p = 0.00004p = 0.0003
Scale: Spiritual Events × Country: Gh.-0.07 (-0.16, 0.01)-0.02 (-0.08, 0.05)-0.05 (-0.14, 0.03)-0.06 (-0.14, 0.03)
p = 0.10p = 0.65p = 0.24p = 0.23
Scale: Spiritual Events × Country: Th.-0.12 (-0.20, -0.04)-0.12 (-0.18, -0.05)-0.13 (-0.21, -0.05)-0.13 (-0.21, -0.05)
p = 0.003p = 0.001p = 0.002p = 0.002
Scale: Spiritual Events × Country: Ch.0.13 (0.02, 0.23)0.20 (0.13, 0.27)0.09 (-0.02, 0.21)0.09 (-0.02, 0.21)
p = 0.02p = 0.00p = 0.11p = 0.13
Scale: Spiritual Events × Country: Va.-0.02 (-0.15, 0.10)-0.14 (-0.21, -0.07)0.01 (-0.13, 0.14)0.01 (-0.12, 0.15)
p = 0.72p = 0.0002p = 0.94p = 0.87
Scale: Spiritual Events × Porosity Scale-0.05 (-0.08, -0.01)-0.01 (-0.06, 0.03)-0.03 (-0.08, 0.02)-0.03 (-0.08, 0.02)
p = 0.01p = 0.57p = 0.21p = 0.20
Gender: male0.02 (-0.04, 0.08)
p = 0.51
Age0.05 (-0.01, 0.11)
p = 0.13
Scale: Spiritual Events × Absorption0.04 (0.01, 0.08)0.04 (0.01, 0.08)0.06 (0.01, 0.11)0.06 (0.01, 0.11)
p = 0.02p = 0.02p = 0.02p = 0.02
Country: Gh. × Porosity Scale-0.10 (-0.22, 0.03)-0.08 (-0.20, 0.05)-0.08 (-0.21, 0.05)
p = 0.14p = 0.25p = 0.22
Country: Th. × Porosity Scale-0.02 (-0.16, 0.11)-0.03 (-0.16, 0.10)-0.04 (-0.17, 0.10)
p = 0.73p = 0.64p = 0.58
Country: Ch. × Porosity Scale0.06 (-0.09, 0.21)0.04 (-0.12, 0.20)0.03 (-0.13, 0.19)
p = 0.43p = 0.59p = 0.73
Country: Va. × Porosity Scale-0.17 (-0.34, 0.01)-0.16 (-0.34, 0.02)-0.15 (-0.33, 0.03)
p = 0.07p = 0.09p = 0.11
Scale: Spiritual Events × Country: Gh. × Porosity Scale0.12 (0.03, 0.20)0.13 (0.04, 0.22)0.13 (0.04, 0.22)
p = 0.01p = 0.004p = 0.004
Scale: Spiritual Events × Country: Th. × Porosity Scale-0.001 (-0.09, 0.09)0.02 (-0.07, 0.11)0.02 (-0.07, 0.11)
p = 0.98p = 0.70p = 0.72
Scale: Spiritual Events × Country: Ch. × Porosity Scale-0.07 (-0.17, 0.03)-0.09 (-0.20, 0.02)-0.09 (-0.20, 0.02)
p = 0.17p = 0.10p = 0.11
Scale: Spiritual Events × Country: Va. × Porosity Scale-0.11 (-0.22, 0.01)-0.11 (-0.23, 0.01)-0.12 (-0.24, 0.003)
p = 0.08p = 0.07p = 0.06
Country: Gh. × Absorption-0.04 (-0.16, 0.07)-0.01 (-0.13, 0.11)-0.01 (-0.13, 0.12)
p = 0.45p = 0.87p = 0.93
Country: Th. × Absorption0.03 (-0.07, 0.14)0.05 (-0.07, 0.16)0.05 (-0.06, 0.17)
p = 0.54p = 0.43p = 0.39
Country: Ch. × Absorption-0.03 (-0.15, 0.08)0.01 (-0.16, 0.18)0.01 (-0.16, 0.18)
p = 0.57p = 0.88p = 0.92
Country: Va. × Absorption-0.07 (-0.18, 0.05)-0.09 (-0.28, 0.10)-0.10 (-0.29, 0.10)
p = 0.26p = 0.38p = 0.33
Scale: Spiritual Events × Country: Gh. × Absorption0.01 (-0.06, 0.08)-0.004 (-0.09, 0.08)-0.004 (-0.09, 0.08)
p = 0.80p = 0.93p = 0.93
Scale: Spiritual Events × Country: Th. × Absorption-0.04 (-0.11, 0.02)-0.05 (-0.12, 0.03)-0.05 (-0.12, 0.03)
p = 0.20p = 0.25p = 0.26
Scale: Spiritual Events × Country: Ch. × Absorption-0.01 (-0.08, 0.06)0.03 (-0.08, 0.15)0.03 (-0.08, 0.15)
p = 0.86p = 0.59p = 0.59
Scale: Spiritual Events × Country: Va. × Absorption0.01 (-0.07, 0.08)0.004 (-0.13, 0.13)-0.005 (-0.14, 0.13)
p = 0.88p = 0.96p = 0.95
Scale: Spiritual Events × Porosity Scale × Absorption0.01 (-0.03, 0.06)0.02 (-0.03, 0.06)
p = 0.54p = 0.49
Country: Gh. × Porosity Scale × Absorption-0.09 (-0.21, 0.03)-0.09 (-0.21, 0.03)
p = 0.16p = 0.16
Country: Th. × Porosity Scale × Absorption0.02 (-0.11, 0.15)0.02 (-0.11, 0.15)
p = 0.80p = 0.79
Country: Ch. × Porosity Scale × Absorption0.06 (-0.09, 0.21)0.04 (-0.11, 0.19)
p = 0.44p = 0.60
Country: Va. × Porosity Scale × Absorption0.04 (-0.14, 0.21)0.04 (-0.13, 0.21)
p = 0.69p = 0.66
Scale: Spiritual Events × Country: Gh. × Porosity Scale × Absorption-0.08 (-0.16, 0.01)-0.08 (-0.16, 0.004)
p = 0.07p = 0.07
Scale: Spiritual Events × Country: Th. × Porosity Scale × Absorption0.07 (-0.02, 0.15)0.07 (-0.02, 0.15)
p = 0.15p = 0.15
Scale: Spiritual Events × Country: Ch. × Porosity Scale × Absorption0.02 (-0.08, 0.12)0.02 (-0.08, 0.12)
p = 0.67p = 0.72
Scale: Spiritual Events × Country: Va. × Porosity Scale × Absorption-0.01 (-0.13, 0.10)-0.01 (-0.13, 0.11)
p = 0.82p = 0.85
Constant-0.001 (-0.06, 0.06)-0.001 (-0.08, 0.08)0.07 (-0.001, 0.15)0.02 (-0.04, 0.07)0.06 (-0.01, 0.14)0.07 (-0.01, 0.14)
p = 0.97p = 0.98p = 0.06p = 0.56p = 0.10p = 0.10
Observations1,0081,0081,0081,0081,008978
Log Likelihood-1,133.48-1,275.26-1,066.20-1,113.46-1,089.04-1,069.60
Akaike Inf. Crit.2,278.962,562.522,176.402,270.932,262.092,227.21
Bayesian Inf. Crit.2,308.462,592.012,284.552,379.082,468.552,442.17
rsq_table_fun(list(#r03_spirit, 
  r04_spirit, r05_spirit, #r06_spirit,
  r07_spirit, r08_spirit, #r09_spirit, 
  r10_spirit, #r11_spirit, 
  r12_spirit))
R-squared Type (1) (2) (3) (4) (5) (6)
Marginal 0.38 0.04 0.51 0.45 0.53 0.53
Conditional 0.67 0.67 0.70 0.70 0.71 0.70

Table S42. Study 4, measures of spiritual presence events: relationships with Porosity Vignettes and Absorption (sample as random effect)

r13_spirit <- lmer(Score ~ Scale
                   + (1 | Country/subject_id),
                   control = lmerControl(optimizer = "bobyqa"),
                   data = d4_fortab_long_spirit)

r14_spirit <- lmer(Score ~ Scale * `Porosity Vignettes`
                   + (1 | Country/subject_id),
                   # + (1 + `Porosity Vignettes` | Country), # adding to above, failed to converge
                   control = lmerControl(optimizer = "bobyqa"),
                   data = d4_fortab_long_spirit)

r15_spirit <- lmer(Score ~ Scale * `Porosity Scale`
                   + (1 | Country/subject_id),
                   # + (1 + `Porosity Scale` | Country), # adding to above, perfectly correlated ranfx
                   control = lmerControl(optimizer = "bobyqa"),
                   data = d4_fortab_long_spirit)

r16_spirit <- lmer(Score ~ Scale * Absorption
                   + (1 | Country/subject_id),
                   # + (1 + Absorption | Country), # adding to above, perfectly correlated ranfx
                   control = lmerControl(optimizer = "bobyqa"),
                   data = d4_fortab_long_spirit)

r17_spirit <- lmer(Score ~ Scale * `Porosity Vignettes` * Absorption
                   + (1 | Country/subject_id),
                   # + (1 + `Porosity Vignettes` * Absorption | Country), # adding to above, perfectly correlated ranfx
                   control = lmerControl(optimizer = "bobyqa"),
                   data = d4_fortab_long_spirit)

r18_spirit <- lmer(Score ~ Scale * `Porosity Scale` * Absorption
                   + (1 | Country/subject_id), 
                   # + (1 + `Porosity Scale` * Absorption | Country), # adding to above, perfectly correlated ranfx
                   control = lmerControl(optimizer = "bobyqa"),
                   data = d4_fortab_long_spirit)

r19_spirit <- lmer(Score ~ Scale * `Porosity Vignettes` * Absorption
                   + Gender + Age
                   + (1 | Country/subject_id),
                   # + (1 + `Porosity Vignettes` * Absorption + Gender + Age | Country),
                   control = lmerControl(optimizer = "bobyqa"),
                   data = d4_fortab_long_spirit)

r20_spirit <- lmer(Score ~ Scale * `Porosity Scale` * Absorption
                   + Gender + Age
                   + (1 | Country/subject_id),
                   # + (1 + `Porosity Scale` * Absorption + Gender + Age | Country),
                   control = lmerControl(optimizer = "bobyqa"),
                   data = d4_fortab_long_spirit)
table_s42 <- stargazer(r13_spirit, r14_spirit, #r15_spirit,
                       r16_spirit, r17_spirit, #r18_spirit,
                       r19_spirit, #r20_spirit,
                       type = "html",
                       ci = T,
                       report = c("vcsp"),
                       digits = 2,
                       digits.extra = 1000,
                       omit.table.layout = "n",
                       order = c("^`Porosity Vignettes`$",
                                 "^Absorption$",
                                 "^`Porosity Vignettes`:Absorption$"),
                       # dep.var.labels.include = F,
                       dep.var.caption = "",
                       single.row = T)
cat(sub_interact_fun(table_s42), 
    file = "./stargazer_tables/table_s42.html")
htmltools::includeHTML("./stargazer_tables/table_s42.html")
Score
(1)(2)(3)(4)(5)
Porosity Vignettes0.29 (0.23, 0.36)0.25 (0.19, 0.32)0.26 (0.19, 0.32)
p = 0.00p = 0.00p = 0.00
Absorption0.20 (0.15, 0.26)0.15 (0.10, 0.20)0.15 (0.09, 0.20)
p = 0.00p = 0.0000001p = 0.0000002
Porosity Vignettes × Absorption-0.03 (-0.08, 0.03)-0.02 (-0.07, 0.04)
p = 0.33p = 0.51
Scale: Spiritual Events-0.001 (-0.04, 0.03)-0.001 (-0.04, 0.03)-0.001 (-0.04, 0.03)-0.0004 (-0.04, 0.04)0.002 (-0.04, 0.04)
p = 0.96p = 0.96p = 0.97p = 0.99p = 0.94
Scale: Spiritual Events × Porosity Vignettes0.01 (-0.03, 0.04)-0.003 (-0.04, 0.03)-0.0002 (-0.04, 0.04)
p = 0.74p = 0.88p = 1.00
Gender: male-0.01 (-0.07, 0.04)
p = 0.63
Age0.06 (-0.003, 0.13)
p = 0.06
Scale: Spiritual Events × Absorption0.04 (0.01, 0.08)0.04 (0.01, 0.08)0.04 (0.003, 0.08)
p = 0.02p = 0.02p = 0.04
Scale: Spiritual Events × Porosity Vignettes × Absorption-0.002 (-0.04, 0.03)-0.002 (-0.04, 0.03)
p = 0.91p = 0.91
Constant0.02 (-0.60, 0.63)0.01 (-0.46, 0.48)0.02 (-0.60, 0.63)0.02 (-0.47, 0.51)0.01 (-0.45, 0.47)
p = 0.96p = 0.97p = 0.96p = 0.95p = 0.97
Observations1,0081,0081,0081,008978
Log Likelihood-1,137.87-1,104.88-1,116.83-1,098.85-1,078.25
Akaike Inf. Crit.2,285.742,223.762,247.672,219.712,182.49
Bayesian Inf. Crit.2,310.322,258.172,282.082,273.782,246.00
rsq_table_fun(list(r13_spirit, r14_spirit, #r15_spirit,
                   r16_spirit, r17_spirit, #r18_spirit,
                   r19_spirit #, r20_spirit
))
R-squared Type (1) (2) (3) (4) (5)
Marginal 0.0 0.09 0.04 0.11 0.12
Conditional 0.7 0.64 0.70 0.65 0.64

Table S43. Study 4, measures of spiritual presence events: relationships with Porosity Scale and Absorption (sample as random effect)

table_s43 <- stargazer(r13_spirit, #r14_spirit, 
                       r15_spirit, r16_spirit, #r17_spirit, 
                       r18_spirit, #r19_spirit, 
                       r20_spirit,
                       type = "html",
                       ci = T,
                       report = c("vcsp"),
                       digits = 2,
                       digits.extra = 1000,
                       omit.table.layout = "n",
                       order = c("^`Porosity Scale`$",
                                 "^Absorption$",
                                 "^`Porosity Scale`:Absorption$"),
                       # dep.var.labels.include = F,
                       dep.var.caption = "",
                       single.row = T)
cat(sub_interact_fun(table_s43), 
    file = "./stargazer_tables/table_s43.html")
htmltools::includeHTML("./stargazer_tables/table_s43.html")
Score
(1)(2)(3)(4)(5)
Porosity Scale0.41 (0.34, 0.47)0.36 (0.30, 0.43)0.37 (0.30, 0.44)
p = 0.00p = 0.00p = 0.00
Absorption0.20 (0.15, 0.26)0.13 (0.08, 0.19)0.13 (0.08, 0.19)
p = 0.00p = 0.0000005p = 0.000002
Porosity Scale × Absorption-0.002 (-0.05, 0.05)-0.002 (-0.05, 0.05)
p = 0.94p = 0.95
Scale: Spiritual Events-0.001 (-0.04, 0.03)-0.001 (-0.04, 0.03)-0.001 (-0.04, 0.03)-0.004 (-0.04, 0.03)-0.002 (-0.04, 0.03)
p = 0.96p = 0.96p = 0.97p = 0.85p = 0.91
Scale: Spiritual Events × Porosity Scale-0.05 (-0.08, -0.01)-0.06 (-0.09, -0.02)-0.06 (-0.09, -0.02)
p = 0.02p = 0.002p = 0.004
Gender: male0.03 (-0.03, 0.08)
p = 0.35
Age0.05 (-0.01, 0.11)
p = 0.11
Scale: Spiritual Events × Absorption0.04 (0.01, 0.08)0.05 (0.02, 0.09)0.05 (0.01, 0.09)
p = 0.02p = 0.004p = 0.01
Scale: Spiritual Events × Porosity Scale × Absorption0.01 (-0.02, 0.05)0.01 (-0.02, 0.05)
p = 0.41p = 0.45
Constant0.02 (-0.60, 0.63)0.01 (-0.38, 0.39)0.02 (-0.60, 0.63)0.01 (-0.40, 0.41)0.01 (-0.37, 0.39)
p = 0.96p = 0.98p = 0.96p = 0.97p = 0.95
Observations1,0081,0081,0081,008978
Log Likelihood-1,137.87-1,076.95-1,116.83-1,071.59-1,051.97
Akaike Inf. Crit.2,285.742,167.902,247.672,165.182,129.93
Bayesian Inf. Crit.2,310.322,202.312,282.082,219.262,193.44
rsq_table_fun(list(r13_spirit, #r14_spirit, 
                   r15_spirit, r16_spirit, #r17_spirit, 
                   r18_spirit, #r19_spirit 
                   r20_spirit
))
R-squared Type (1) (2) (3) (4) (5)
Marginal 0.0 0.19 0.04 0.20 0.22
Conditional 0.7 0.62 0.70 0.63 0.62

Table S44. Study 4, measures of spiritual presence events: relationships with Porosity Vignettes by country

r21_spirit_us <- lmer(Score ~ Scale * `Porosity Vignettes` 
                      + (1 | subject_id),
                      control = lmerControl(optimizer = "bobyqa"),
                      data = d4_fortab_long_spirit %>% filter(Country == "US") %>%
                        mutate_at(vars(Score, `Porosity Vignettes`), ~ scale(.)))

r21_spirit_gh <- lmer(Score ~ Scale * `Porosity Vignettes` 
                      + (1 | subject_id),
                      control = lmerControl(optimizer = "bobyqa"),
                      data = d4_fortab_long_spirit %>% filter(Country == "Ghana") %>%
                        mutate_at(vars(Score, ,`Porosity Vignettes`), ~ scale(.)))

r21_spirit_th <- lmer(Score ~ Scale * `Porosity Vignettes` 
                      + (1 | subject_id),
                      control = lmerControl(optimizer = "bobyqa"),
                      data = d4_fortab_long_spirit %>% filter(Country == "Thailand") %>%
                        mutate_at(vars(Score, `Porosity Vignettes`), ~ scale(.)))

r21_spirit_ch <- lmer(Score ~ Scale * `Porosity Vignettes` 
                      + (1 | subject_id),
                      control = lmerControl(optimizer = "bobyqa"),
                      data = d4_fortab_long_spirit %>% filter(Country == "China") %>%
                        mutate_at(vars(Score, `Porosity Vignettes`), ~ scale(.)))

r21_spirit_vt <- lmer(Score ~ Scale * `Porosity Vignettes` 
                      + (1 | subject_id),
                      control = lmerControl(optimizer = "bobyqa"),
                      data = d4_fortab_long_spirit %>% filter(Country == "Vanuatu") %>%
                        mutate_at(vars(Score, `Porosity Vignettes`), ~ scale(.)))
table_s44 <- stargazer(r21_spirit_us, r21_spirit_gh, r21_spirit_th, 
                       r21_spirit_ch, r21_spirit_vt, 
                       type = "html",
                       ci = T,
                       report = c("vcsp"),
                       digits = 2,
                       digits.extra = 1000,
                       omit.table.layout = "n",
                       order = c("^`Porosity Vignettes`$"),
                       # dep.var.labels.include = F,
                       dep.var.caption = "",
                       single.row = T)
cat(sub_interact_fun(table_s44), 
    file = "./stargazer_tables/table_s44.html")
htmltools::includeHTML("./stargazer_tables/table_s44.html")
Score
(1)(2)(3)(4)(5)
Porosity Vignettes0.53 (0.38, 0.67)0.17 (0.02, 0.33)0.28 (0.13, 0.44)0.34 (0.19, 0.48)0.12 (-0.05, 0.28)
p = 0.00p = 0.03p = 0.0003p = 0.00001p = 0.18
Scale: Spiritual Events0.07 (-0.01, 0.14)-0.02 (-0.13, 0.10)-0.18 (-0.26, -0.09)0.33 (0.23, 0.43)-0.20 (-0.31, -0.08)
p = 0.08p = 0.77p = 0.0001p = 0.00p = 0.001
Scale: Spiritual Events × Porosity Vignettes0.13 (0.05, 0.20)0.13 (0.01, 0.24)0.04 (-0.05, 0.13)-0.08 (-0.17, 0.02)0.01 (-0.10, 0.13)
p = 0.001p = 0.04p = 0.36p = 0.12p = 0.87
Constant-0.00 (-0.15, 0.15)-0.003 (-0.16, 0.15)-0.00 (-0.15, 0.15)-0.0001 (-0.14, 0.14)0.00 (-0.16, 0.16)
p = 1.00p = 0.97p = 1.00p = 1.00p = 1.00
Observations204199222199184
Log Likelihood-238.99-280.13-292.61-255.25-256.66
Akaike Inf. Crit.489.98572.26597.23522.50525.33
Bayesian Inf. Crit.509.89592.02617.64542.26544.62
rsq_table_fun(list(r21_spirit_us, r21_spirit_gh, r21_spirit_th, 
                   r21_spirit_ch, r21_spirit_vt))
R-squared Type (1) (2) (3) (4) (5)
Marginal 0.30 0.05 0.11 0.23 0.05
Conditional 0.71 0.32 0.56 0.53 0.38

Table S45. Study 4, measures of spiritual presence events: relationships with Porosity Scale by country

r22_spirit_us <- lmer(Score ~ Scale * `Porosity Scale` 
                      + (1 | subject_id),
                      control = lmerControl(optimizer = "bobyqa"),
                      data = d4_fortab_long_spirit %>% filter(Country == "US") %>%
                        mutate_at(vars(Score, `Porosity Scale`), ~ scale(.)))

r22_spirit_gh <- lmer(Score ~ Scale * `Porosity Scale` 
                      + (1 | subject_id),
                      control = lmerControl(optimizer = "bobyqa"),
                      data = d4_fortab_long_spirit %>% filter(Country == "Ghana") %>%
                        mutate_at(vars(Score, `Porosity Scale`), ~ scale(.)))

r22_spirit_th <- lmer(Score ~ Scale * `Porosity Scale` 
                      + (1 | subject_id),
                      control = lmerControl(optimizer = "bobyqa"),
                      data = d4_fortab_long_spirit %>% filter(Country == "Thailand") %>%
                        mutate_at(vars(Score, `Porosity Scale`), ~ scale(.)))

r22_spirit_ch <- lmer(Score ~ Scale * `Porosity Scale` 
                      + (1 | subject_id),
                      control = lmerControl(optimizer = "bobyqa"),
                      data = d4_fortab_long_spirit %>% filter(Country == "China") %>%
                        mutate_at(vars(Score, `Porosity Scale`), ~ scale(.)))

r22_spirit_vt <- lmer(Score ~ Scale * `Porosity Scale` 
                      + (1 | subject_id),
                      control = lmerControl(optimizer = "bobyqa"),
                      data = d4_fortab_long_spirit %>% filter(Country == "Vanuatu") %>%
                        mutate_at(vars(Score, `Porosity Scale`), ~ scale(.)))
table_s45 <- stargazer(r22_spirit_us, r22_spirit_gh, r22_spirit_th, 
                       r22_spirit_ch, r22_spirit_vt, 
                       type = "html",
                       ci = T,
                       report = c("vcsp"),
                       digits = 2,
                       digits.extra = 1000,
                       omit.table.layout = "n",
                       order = c("^`Porosity Scale`$"),
                       # dep.var.labels.include = F,
                       dep.var.caption = "",
                       single.row = T)
cat(sub_interact_fun(table_s45), 
    file = "./stargazer_tables/table_s45.html")
htmltools::includeHTML("./stargazer_tables/table_s45.html")
Score
(1)(2)(3)(4)(5)
Porosity Scale0.56 (0.41, 0.70)0.27 (0.12, 0.42)0.40 (0.25, 0.54)0.47 (0.34, 0.60)0.16 (-0.002, 0.32)
p = 0.00p = 0.0004p = 0.0000001p = 0.00p = 0.06
Scale: Spiritual Events0.07 (-0.01, 0.15)-0.02 (-0.13, 0.10)-0.18 (-0.27, -0.09)0.33 (0.23, 0.43)-0.20 (-0.31, -0.08)
p = 0.09p = 0.77p = 0.0001p = 0.00p = 0.001
Scale: Spiritual Events × Porosity Scale0.04 (-0.03, 0.12)0.10 (-0.01, 0.22)-0.02 (-0.11, 0.07)-0.09 (-0.19, 0.002)-0.10 (-0.21, 0.02)
p = 0.27p = 0.09p = 0.70p = 0.06p = 0.10
Constant0.00 (-0.14, 0.14)-0.003 (-0.15, 0.15)-0.00 (-0.14, 0.14)-0.001 (-0.13, 0.13)0.00 (-0.16, 0.16)
p = 1.00p = 0.97p = 1.00p = 1.00p = 1.00
Observations204199222199184
Log Likelihood-241.07-277.32-286.41-243.43-254.39
Akaike Inf. Crit.494.14566.64584.81498.86520.79
Bayesian Inf. Crit.514.05586.40605.23518.62540.08
rsq_table_fun(list(r22_spirit_us, r22_spirit_gh, r22_spirit_th, 
                   r22_spirit_ch, r22_spirit_vt))
R-squared Type (1) (2) (3) (4) (5)
Marginal 0.31 0.08 0.19 0.34 0.07
Conditional 0.68 0.31 0.55 0.53 0.40

Table S46. Study 4, measures of spiritual presence events: relationships with Absorption by country

r23_spirit_us <- lmer(Score ~ Scale * `Absorption` 
                      + (1 | subject_id),
                      control = lmerControl(optimizer = "bobyqa"),
                      data = d4_fortab_long_spirit %>% filter(Country == "US") %>%
                        mutate_at(vars(Score, `Absorption`), ~ scale(.)))

r23_spirit_gh <- lmer(Score ~ Scale * `Absorption` 
                      + (1 | subject_id),
                      control = lmerControl(optimizer = "bobyqa"),
                      data = d4_fortab_long_spirit %>% filter(Country == "Ghana") %>%
                        mutate_at(vars(Score, `Absorption`), ~ scale(.)))

r23_spirit_th <- lmer(Score ~ Scale * `Absorption` 
                      + (1 | subject_id),
                      control = lmerControl(optimizer = "bobyqa"),
                      data = d4_fortab_long_spirit %>% filter(Country == "Thailand") %>%
                        mutate_at(vars(Score, `Absorption`), ~ scale(.)))

r23_spirit_ch <- lmer(Score ~ Scale * `Absorption` 
                      + (1 | subject_id),
                      control = lmerControl(optimizer = "bobyqa"),
                      data = d4_fortab_long_spirit %>% filter(Country == "China") %>%
                        mutate_at(vars(Score, `Absorption`), ~ scale(.)))

r23_spirit_vt <- lmer(Score ~ Scale * `Absorption` 
                      + (1 | subject_id),
                      control = lmerControl(optimizer = "bobyqa"),
                      data = d4_fortab_long_spirit %>% filter(Country == "Vanuatu") %>%
                        mutate_at(vars(Score, `Absorption`), ~ scale(.)))
table_s46 <- stargazer(r23_spirit_us, r23_spirit_gh, r23_spirit_th, 
                       r23_spirit_ch, r23_spirit_vt, 
                       type = "html",
                       ci = T,
                       report = c("vcsp"),
                       digits = 2,
                       digits.extra = 1000,
                       omit.table.layout = "n",
                       order = c("^Absorption$"),
                       # dep.var.labels.include = F,
                       dep.var.caption = "",
                       single.row = T)
cat(sub_interact_fun(table_s46), 
    file = "./stargazer_tables/table_s46.html")
htmltools::includeHTML("./stargazer_tables/table_s46.html")
Score
(1)(2)(3)(4)(5)
Absorption0.30 (0.13, 0.47)0.18 (0.03, 0.34)0.36 (0.21, 0.50)0.28 (0.13, 0.43)0.19 (0.03, 0.35)
p = 0.0005p = 0.03p = 0.000003p = 0.0003p = 0.03
Scale: Spiritual Events0.07 (-0.01, 0.15)-0.02 (-0.14, 0.10)-0.18 (-0.27, -0.09)0.33 (0.24, 0.43)-0.20 (-0.31, -0.08)
p = 0.09p = 0.77p = 0.0001p = 0.00p = 0.001
Scale: Spiritual Events × Absorption0.08 (0.001, 0.15)0.06 (-0.06, 0.18)0.001 (-0.09, 0.09)0.06 (-0.03, 0.16)0.07 (-0.04, 0.18)
p = 0.05p = 0.31p = 0.99p = 0.21p = 0.23
Constant-0.00 (-0.17, 0.17)-0.003 (-0.16, 0.15)-0.00 (-0.15, 0.15)0.002 (-0.15, 0.15)0.00 (-0.16, 0.16)
p = 1.00p = 0.97p = 1.00p = 0.98p = 1.00
Observations204199222199184
Log Likelihood-257.02-281.60-289.18-258.75-254.37
Akaike Inf. Crit.526.03575.21590.36529.50520.74
Bayesian Inf. Crit.545.94594.97610.78549.26540.03
rsq_table_fun(list(r23_spirit_us, r23_spirit_gh, r23_spirit_th, 
                   r23_spirit_ch, r23_spirit_vt))
R-squared Type (1) (2) (3) (4) (5)
Marginal 0.10 0.04 0.16 0.19 0.08
Conditional 0.69 0.29 0.55 0.52 0.39

Relationships: Secular anomalous events

Setup

# longform dataframe
d4_fortab_long_secular <- d4_abs_omit %>% 
  gather(Scale, Score, 
         c(Hallucinations, Paranormal)) %>%
  mutate(Scale = factor(Scale))
# set contrasts
contrasts(d4_fortab_long_secular$Country) <- cbind(": Gh." = c(-1, 1, 0, 0, 0),
                                                   ": Th." = c(-1, 0, 1, 0, 0),
                                                   ": Ch." = c(-1, 0, 0, 1, 0),
                                                   ": Va." = c(-1, 0, 0, 0, 1))
contrasts(d4_fortab_long_secular$Gender) <- cbind(": male" = c(-1, 1))
contrasts(d4_fortab_long_secular$Scale) <- cbind(": Hallucinations" = c(1, -1))

Table S47. Study 4, measures of secular anomalous events: relationships with Porosity Vignettes and Absorption (sample as fixed effect)

r03_secular <- lmer(Score ~ Scale * `Porosity Vignettes`
                    + (1 | subject_id),
                    control = lmerControl(optimizer = "bobyqa"),
                    data = d4_fortab_long_secular)

r04_secular <- lmer(Score ~ Scale * `Porosity Scale`
                    + (1 | subject_id),
                    control = lmerControl(optimizer = "bobyqa"),
                    data = d4_fortab_long_secular)

r05_secular <- lmer(Score ~ Scale * Absorption
                    + (1 | subject_id),
                    control = lmerControl(optimizer = "bobyqa"),
                    data = d4_fortab_long_secular)

r06_secular <- lmer(Score ~ Scale * Country * `Porosity Vignettes`
                    + (1 | subject_id),
                    control = lmerControl(optimizer = "bobyqa"),
                    data = d4_fortab_long_secular)

r07_secular <- lmer(Score ~ Scale * Country * `Porosity Scale`
                    + (1 | subject_id),
                    control = lmerControl(optimizer = "bobyqa"),
                    data = d4_fortab_long_secular)

r08_secular <- lmer(Score ~ Scale * Country * Absorption
                    + (1 | subject_id),
                    control = lmerControl(optimizer = "bobyqa"),
                    data = d4_fortab_long_secular)

r09_secular <- lmer(Score ~ Scale * Country * `Porosity Vignettes` * Absorption
                    + (1 | subject_id),
                    control = lmerControl(optimizer = "bobyqa"),
                    data = d4_fortab_long_secular)

r10_secular <- lmer(Score ~ Scale * Country * `Porosity Scale` * Absorption
                    + (1 | subject_id),
                    control = lmerControl(optimizer = "bobyqa"),
                    data = d4_fortab_long_secular)

r11_secular <- lmer(Score ~ Scale * Country * `Porosity Vignettes` * Absorption
                    + Gender + Age
                    + (1 | subject_id),
                    control = lmerControl(optimizer = "bobyqa"),
                    data = d4_fortab_long_secular)

r12_secular <- lmer(Score ~ Scale * Country * `Porosity Scale` * Absorption
                    + Gender + Age
                    + (1 | subject_id),
                    control = lmerControl(optimizer = "bobyqa"),
                    data = d4_fortab_long_secular)
table_s47 <- stargazer(r03_secular, #r04_secular, 
                       r05_secular, r06_secular, #r07_secular, 
                       r08_secular, r09_secular, #r10_secular,
                       r11_secular, #r12_secular, 
                       type = "html",
                       ci = T,
                       report = c("vcsp"),
                       digits = 2,
                       digits.extra = 1000,
                       omit.table.layout = "n",
                       order = c("^`Porosity Vignettes`$",
                                 "^Absorption$",
                                 "^`Porosity Vignettes`:Absorption$"),
                       # dep.var.labels.include = F,
                       dep.var.caption = "",
                       single.row = T) 
cat(sub_interact_fun(table_s47), 
    file = "./stargazer_tables/table_s47.html")
htmltools::includeHTML("./stargazer_tables/table_s47.html")
Score
(1)(2)(3)(4)(5)(6)
Porosity Vignettes0.31 (0.24, 0.37)0.34 (0.26, 0.41)0.26 (0.19, 0.34)0.26 (0.19, 0.34)
p = 0.00p = 0.00p = 0.00p = 0.00
Absorption0.34 (0.27, 0.40)0.33 (0.27, 0.40)0.32 (0.24, 0.39)0.30 (0.22, 0.37)
p = 0.00p = 0.00p = 0.00p = 0.00
Porosity Vignettes × Absorption-0.002 (-0.08, 0.07)0.01 (-0.06, 0.08)
p = 0.96p = 0.81
Scale: Hallucinations0.00 (-0.05, 0.05)0.00 (-0.05, 0.05)-0.07 (-0.13, -0.01)0.002 (-0.05, 0.05)-0.06 (-0.13, -0.0001)-0.07 (-0.13, -0.001)
p = 1.00p = 1.00p = 0.03p = 0.96p = 0.05p = 0.05
Country: Gh.0.01 (-0.13, 0.16)0.24 (0.12, 0.37)0.07 (-0.07, 0.21)0.12 (-0.03, 0.26)
p = 0.85p = 0.0002p = 0.32p = 0.12
Country: Th.-0.31 (-0.44, -0.18)-0.25 (-0.37, -0.13)-0.34 (-0.46, -0.22)-0.35 (-0.48, -0.23)
p = 0.000004p = 0.00003p = 0.0000001p = 0.00000004
Country: Ch.0.24 (0.06, 0.42)-0.09 (-0.22, 0.03)0.20 (0.04, 0.37)0.20 (0.03, 0.37)
p = 0.01p = 0.14p = 0.02p = 0.02
Country: Va.-0.12 (-0.29, 0.05)-0.01 (-0.14, 0.11)-0.08 (-0.24, 0.08)-0.05 (-0.21, 0.12)
p = 0.17p = 0.84p = 0.33p = 0.56
Scale: Hallucinations × Country: Gh.0.11 (-0.01, 0.23)0.09 (-0.02, 0.19)0.10 (-0.02, 0.22)0.10 (-0.02, 0.23)
p = 0.09p = 0.10p = 0.12p = 0.12
Scale: Hallucinations × Country: Th.-0.12 (-0.23, -0.02)-0.17 (-0.27, -0.08)-0.12 (-0.23, -0.01)-0.12 (-0.23, -0.01)
p = 0.03p = 0.001p = 0.04p = 0.04
Scale: Hallucinations × Country: Ch.-0.26 (-0.41, -0.12)-0.07 (-0.18, 0.03)-0.27 (-0.42, -0.12)-0.27 (-0.42, -0.12)
p = 0.0005p = 0.16p = 0.0004p = 0.0004
Scale: Hallucinations × Country: Va.-0.04 (-0.17, 0.10)-0.08 (-0.19, 0.02)-0.04 (-0.18, 0.10)-0.03 (-0.18, 0.12)
p = 0.60p = 0.13p = 0.58p = 0.69
Scale: Hallucinations × Porosity Vignettes-0.01 (-0.07, 0.04)-0.07 (-0.13, -0.005)-0.07 (-0.14, -0.01)-0.07 (-0.14, -0.01)
p = 0.60p = 0.04p = 0.03p = 0.04
Gender: male-0.06 (-0.12, 0.005)
p = 0.08
Age-0.05 (-0.12, 0.02)
p = 0.17
Scale: Hallucinations × Absorption0.01 (-0.04, 0.07)0.01 (-0.04, 0.06)0.02 (-0.05, 0.08)0.02 (-0.05, 0.09)
p = 0.63p = 0.61p = 0.61p = 0.59
Country: Gh. × Porosity Vignettes-0.12 (-0.26, 0.01)-0.10 (-0.23, 0.03)-0.10 (-0.22, 0.03)
p = 0.08p = 0.12p = 0.13
Country: Th. × Porosity Vignettes-0.12 (-0.27, 0.02)-0.15 (-0.28, -0.01)-0.15 (-0.29, -0.02)
p = 0.10p = 0.04p = 0.03
Country: Ch. × Porosity Vignettes0.28 (0.09, 0.47)0.32 (0.14, 0.49)0.30 (0.12, 0.48)
p = 0.005p = 0.001p = 0.001
Country: Va. × Porosity Vignettes-0.33 (-0.50, -0.15)-0.31 (-0.47, -0.14)-0.33 (-0.50, -0.16)
p = 0.0003p = 0.0003p = 0.0002
Scale: Hallucinations × Country: Gh. × Porosity Vignettes0.16 (0.05, 0.27)0.15 (0.04, 0.27)0.15 (0.04, 0.27)
p = 0.005p = 0.01p = 0.01
Scale: Hallucinations × Country: Th. × Porosity Vignettes-0.05 (-0.16, 0.07)-0.04 (-0.16, 0.08)-0.04 (-0.16, 0.08)
p = 0.43p = 0.51p = 0.51
Scale: Hallucinations × Country: Ch. × Porosity Vignettes-0.30 (-0.46, -0.15)-0.30 (-0.46, -0.14)-0.29 (-0.45, -0.13)
p = 0.0002p = 0.0003p = 0.0004
Scale: Hallucinations × Country: Va. × Porosity Vignettes0.11 (-0.03, 0.26)0.11 (-0.03, 0.26)0.12 (-0.04, 0.27)
p = 0.12p = 0.13p = 0.14
Country: Gh. × Absorption-0.09 (-0.21, 0.04)-0.07 (-0.20, 0.07)-0.05 (-0.18, 0.08)
p = 0.17p = 0.33p = 0.47
Country: Th. × Absorption-0.01 (-0.13, 0.11)-0.01 (-0.14, 0.11)0.001 (-0.12, 0.12)
p = 0.91p = 0.83p = 0.99
Country: Ch. × Absorption-0.03 (-0.16, 0.09)0.07 (-0.12, 0.25)0.09 (-0.09, 0.27)
p = 0.62p = 0.49p = 0.33
Country: Va. × Absorption-0.11 (-0.23, 0.02)-0.04 (-0.20, 0.12)-0.05 (-0.22, 0.11)
p = 0.11p = 0.65p = 0.53
Scale: Hallucinations × Country: Gh. × Absorption0.05 (-0.05, 0.15)0.02 (-0.10, 0.14)0.02 (-0.10, 0.14)
p = 0.33p = 0.73p = 0.75
Scale: Hallucinations × Country: Th. × Absorption-0.04 (-0.13, 0.06)-0.02 (-0.13, 0.09)-0.02 (-0.13, 0.09)
p = 0.48p = 0.73p = 0.73
Scale: Hallucinations × Country: Ch. × Absorption-0.01 (-0.11, 0.09)0.02 (-0.15, 0.18)0.01 (-0.15, 0.18)
p = 0.82p = 0.86p = 0.89
Scale: Hallucinations × Country: Va. × Absorption-0.01 (-0.12, 0.10)-0.001 (-0.14, 0.14)0.03 (-0.12, 0.18)
p = 0.86p = 1.00p = 0.73
Scale: Hallucinations × Porosity Vignettes × Absorption-0.02 (-0.09, 0.04)-0.02 (-0.09, 0.04)
p = 0.53p = 0.49
Country: Gh. × Porosity Vignettes × Absorption-0.11 (-0.23, 0.01)-0.13 (-0.25, -0.003)
p = 0.08p = 0.05
Country: Th. × Porosity Vignettes × Absorption0.04 (-0.09, 0.18)0.03 (-0.11, 0.16)
p = 0.55p = 0.69
Country: Ch. × Porosity Vignettes × Absorption0.14 (-0.05, 0.34)0.13 (-0.06, 0.32)
p = 0.15p = 0.17
Country: Va. × Porosity Vignettes × Absorption-0.07 (-0.22, 0.09)-0.05 (-0.21, 0.11)
p = 0.41p = 0.56
Scale: Hallucinations × Country: Gh. × Porosity Vignettes × Absorption0.04 (-0.07, 0.15)0.04 (-0.07, 0.16)
p = 0.46p = 0.44
Scale: Hallucinations × Country: Th. × Porosity Vignettes × Absorption-0.04 (-0.16, 0.08)-0.04 (-0.16, 0.08)
p = 0.49p = 0.52
Scale: Hallucinations × Country: Ch. × Porosity Vignettes × Absorption0.04 (-0.13, 0.21)0.05 (-0.12, 0.22)
p = 0.66p = 0.59
Scale: Hallucinations × Country: Va. × Porosity Vignettes × Absorption-0.004 (-0.14, 0.14)-0.03 (-0.18, 0.12)
p = 0.96p = 0.70
Constant-0.00 (-0.06, 0.06)0.00 (-0.06, 0.06)0.10 (0.03, 0.18)0.005 (-0.06, 0.07)0.11 (0.03, 0.18)0.09 (0.02, 0.16)
p = 1.00p = 1.00p = 0.01p = 0.88p = 0.004p = 0.02
Observations1,0101,0101,0101,0101,010980
Log Likelihood-1,380.61-1,372.19-1,351.32-1,365.54-1,347.04-1,307.43
Akaike Inf. Crit.2,773.232,756.372,746.632,775.072,778.082,702.87
Bayesian Inf. Crit.2,802.742,785.882,854.822,883.262,984.622,917.92
rsq_table_fun(list(r03_secular, #r04_secular, 
                   r05_secular, r06_secular, #r07_secular, 
                   r08_secular, r09_secular, #r10_secular,
                   r11_secular #, r12_secular 
))
R-squared Type (1) (2) (3) (4) (5) (6)
Marginal 0.09 0.11 0.20 0.18 0.27 0.27
Conditional 0.29 0.29 0.36 0.33 0.36 0.34

Table S48. Study 4, measures of secular anomalous events: relationships with Porosity Scale and Absorption (sample as fixed effect)

table_s48 <- stargazer(#r03_secular, 
  r04_secular, r05_secular, #r06_secular,
  r07_secular, r08_secular, #r09_secular, 
  r10_secular, #r11_secular, 
  r12_secular, 
  type = "html",
  ci = T,
  report = c("vcsp"),
  digits = 2,
  digits.extra = 1000,
  omit.table.layout = "n",
  order = c("^`Porosity Scale`$",
            "^Absorption$",
            "^`Porosity Scale`:Absorption$"),
  # dep.var.labels.include = F,
  dep.var.caption = "",
  single.row = T) 
cat(sub_interact_fun(table_s48), 
    file = "./stargazer_tables/table_s48.html")
htmltools::includeHTML("./stargazer_tables/table_s48.html")
Score
(1)(2)(3)(4)(5)(6)
Porosity Scale0.33 (0.26, 0.39)0.45 (0.37, 0.53)0.37 (0.29, 0.45)0.36 (0.28, 0.45)
p = 0.00p = 0.00p = 0.00p = 0.00
Absorption0.34 (0.27, 0.40)0.33 (0.27, 0.40)0.27 (0.19, 0.35)0.26 (0.18, 0.34)
p = 0.00p = 0.00p = 0.00p = 0.00
Porosity Scale × Absorption0.05 (-0.03, 0.12)0.04 (-0.04, 0.12)
p = 0.25p = 0.32
Scale: Hallucinations-0.00 (-0.05, 0.05)0.00 (-0.05, 0.05)-0.10 (-0.17, -0.03)0.002 (-0.05, 0.05)-0.12 (-0.19, -0.04)-0.11 (-0.19, -0.04)
p = 1.00p = 1.00p = 0.005p = 0.96p = 0.003p = 0.004
Country: Gh.0.05 (-0.10, 0.20)0.24 (0.12, 0.37)0.11 (-0.03, 0.25)0.15 (0.004, 0.30)
p = 0.54p = 0.0002p = 0.13p = 0.05
Country: Th.-0.25 (-0.38, -0.11)-0.25 (-0.37, -0.13)-0.27 (-0.40, -0.14)-0.27 (-0.41, -0.14)
p = 0.0004p = 0.00003p = 0.00004p = 0.0001
Country: Ch.0.39 (0.21, 0.58)-0.09 (-0.22, 0.03)0.33 (0.14, 0.51)0.31 (0.12, 0.50)
p = 0.00004p = 0.14p = 0.001p = 0.002
Country: Va.-0.42 (-0.64, -0.19)-0.01 (-0.14, 0.11)-0.35 (-0.57, -0.14)-0.33 (-0.55, -0.11)
p = 0.0003p = 0.84p = 0.002p = 0.004
Scale: Hallucinations × Country: Gh.0.20 (0.08, 0.32)0.09 (-0.02, 0.19)0.23 (0.10, 0.36)0.23 (0.10, 0.36)
p = 0.002p = 0.10p = 0.0005p = 0.001
Scale: Hallucinations × Country: Th.-0.13 (-0.24, -0.02)-0.17 (-0.27, -0.08)-0.12 (-0.24, -0.01)-0.13 (-0.24, -0.01)
p = 0.03p = 0.001p = 0.04p = 0.04
Scale: Hallucinations × Country: Ch.-0.35 (-0.51, -0.20)-0.07 (-0.18, 0.03)-0.41 (-0.58, -0.24)-0.41 (-0.58, -0.24)
p = 0.00001p = 0.16p = 0.000002p = 0.000003
Scale: Hallucinations × Country: Va.-0.05 (-0.24, 0.13)-0.08 (-0.19, 0.02)-0.05 (-0.24, 0.15)-0.03 (-0.23, 0.16)
p = 0.58p = 0.13p = 0.64p = 0.74
Scale: Hallucinations × Porosity Scale-0.07 (-0.12, -0.01)-0.13 (-0.20, -0.06)-0.15 (-0.22, -0.07)-0.15 (-0.22, -0.08)
p = 0.02p = 0.0003p = 0.0001p = 0.0001
Gender: male-0.01 (-0.08, 0.05)
p = 0.65
Age-0.06 (-0.13, 0.01)
p = 0.10
Scale: Hallucinations × Absorption0.01 (-0.04, 0.07)0.01 (-0.04, 0.06)0.06 (-0.02, 0.13)0.05 (-0.02, 0.13)
p = 0.63p = 0.61p = 0.14p = 0.17
Country: Gh. × Porosity Scale-0.27 (-0.42, -0.13)-0.25 (-0.38, -0.11)-0.24 (-0.38, -0.10)
p = 0.0003p = 0.001p = 0.001
Country: Th. × Porosity Scale-0.10 (-0.25, 0.06)-0.09 (-0.24, 0.05)-0.09 (-0.24, 0.05)
p = 0.23p = 0.22p = 0.22
Country: Ch. × Porosity Scale0.26 (0.08, 0.44)0.25 (0.07, 0.43)0.25 (0.07, 0.43)
p = 0.005p = 0.01p = 0.01
Country: Va. × Porosity Scale-0.12 (-0.33, 0.08)-0.11 (-0.30, 0.09)-0.10 (-0.30, 0.10)
p = 0.25p = 0.28p = 0.34
Scale: Hallucinations × Country: Gh. × Porosity Scale0.11 (-0.01, 0.23)0.11 (-0.01, 0.24)0.12 (-0.01, 0.24)
p = 0.07p = 0.08p = 0.08
Scale: Hallucinations × Country: Th. × Porosity Scale-0.08 (-0.20, 0.05)-0.06 (-0.19, 0.07)-0.06 (-0.20, 0.07)
p = 0.24p = 0.36p = 0.36
Scale: Hallucinations × Country: Ch. × Porosity Scale-0.34 (-0.49, -0.19)-0.39 (-0.55, -0.23)-0.38 (-0.54, -0.22)
p = 0.00001p = 0.000002p = 0.000004
Scale: Hallucinations × Country: Va. × Porosity Scale0.21 (0.04, 0.38)0.23 (0.05, 0.41)0.23 (0.05, 0.41)
p = 0.02p = 0.02p = 0.02
Country: Gh. × Absorption-0.09 (-0.21, 0.04)-0.004 (-0.14, 0.13)0.003 (-0.13, 0.14)
p = 0.17p = 0.96p = 0.97
Country: Th. × Absorption-0.01 (-0.13, 0.11)0.04 (-0.09, 0.16)0.04 (-0.09, 0.17)
p = 0.91p = 0.58p = 0.53
Country: Ch. × Absorption-0.03 (-0.16, 0.09)-0.07 (-0.26, 0.12)-0.05 (-0.24, 0.14)
p = 0.62p = 0.48p = 0.60
Country: Va. × Absorption-0.11 (-0.23, 0.02)-0.15 (-0.36, 0.06)-0.15 (-0.37, 0.06)
p = 0.11p = 0.16p = 0.16
Scale: Hallucinations × Country: Gh. × Absorption0.05 (-0.05, 0.15)0.03 (-0.09, 0.15)0.04 (-0.09, 0.16)
p = 0.33p = 0.60p = 0.56
Scale: Hallucinations × Country: Th. × Absorption-0.04 (-0.13, 0.06)-0.04 (-0.15, 0.08)-0.03 (-0.15, 0.08)
p = 0.48p = 0.53p = 0.59
Scale: Hallucinations × Country: Ch. × Absorption-0.01 (-0.11, 0.09)0.11 (-0.05, 0.28)0.12 (-0.05, 0.29)
p = 0.82p = 0.19p = 0.18
Scale: Hallucinations × Country: Va. × Absorption-0.01 (-0.12, 0.10)-0.08 (-0.27, 0.11)-0.06 (-0.26, 0.13)
p = 0.86p = 0.40p = 0.53
Scale: Hallucinations × Porosity Scale × Absorption0.01 (-0.05, 0.08)0.01 (-0.06, 0.08)
p = 0.70p = 0.81
Country: Gh. × Porosity Scale × Absorption-0.14 (-0.27, -0.01)-0.14 (-0.27, -0.01)
p = 0.04p = 0.04
Country: Th. × Porosity Scale × Absorption0.07 (-0.07, 0.21)0.08 (-0.06, 0.22)
p = 0.32p = 0.29
Country: Ch. × Porosity Scale × Absorption-0.02 (-0.18, 0.15)-0.004 (-0.17, 0.16)
p = 0.86p = 0.97
Country: Va. × Porosity Scale × Absorption0.04 (-0.15, 0.23)0.04 (-0.15, 0.23)
p = 0.67p = 0.66
Scale: Hallucinations × Country: Gh. × Porosity Scale × Absorption-0.05 (-0.17, 0.06)-0.05 (-0.17, 0.07)
p = 0.37p = 0.42
Scale: Hallucinations × Country: Th. × Porosity Scale × Absorption0.02 (-0.10, 0.15)0.03 (-0.10, 0.16)
p = 0.73p = 0.63
Scale: Hallucinations × Country: Ch. × Porosity Scale × Absorption0.05 (-0.10, 0.20)0.06 (-0.09, 0.21)
p = 0.52p = 0.44
Scale: Hallucinations × Country: Va. × Porosity Scale × Absorption0.01 (-0.16, 0.18)0.002 (-0.17, 0.17)
p = 0.94p = 0.98
Constant0.00 (-0.06, 0.06)0.00 (-0.06, 0.06)0.10 (0.01, 0.18)0.005 (-0.06, 0.07)0.08 (0.002, 0.17)0.08 (-0.004, 0.16)
p = 1.00p = 1.00p = 0.03p = 0.88p = 0.05p = 0.07
Observations1,0101,0101,0101,0101,010980
Log Likelihood-1,371.56-1,372.19-1,328.58-1,365.54-1,319.94-1,290.48
Akaike Inf. Crit.2,755.122,756.372,701.172,775.072,723.892,668.97
Bayesian Inf. Crit.2,784.632,785.882,809.362,883.262,930.432,884.02
rsq_table_fun(list(#r03_secular, 
  r04_secular, r05_secular, #r06_secular,
  r07_secular, r08_secular, #r09_secular, 
  r10_secular, #r11_secular, 
  r12_secular))
R-squared Type (1) (2) (3) (4) (5) (6)
Marginal 0.11 0.11 0.23 0.18 0.31 0.30
Conditional 0.30 0.29 0.38 0.33 0.38 0.37

Table S49. Study 4, measures of secular anomalous events: relationships with Porosity Vignettes and Absorption (sample as random effect)

r13_secular <- lmer(Score ~ Scale
                    + (1 | Country/subject_id),
                    control = lmerControl(optimizer = "bobyqa"),
                    data = d4_fortab_long_secular)

r14_secular <- lmer(Score ~ Scale * `Porosity Vignettes`
                    + (1 | Country/subject_id),
                    # + (1 + `Porosity Vignettes` | Country), # adding to above, failed to converge
                    control = lmerControl(optimizer = "bobyqa"),
                    data = d4_fortab_long_secular)

r15_secular <- lmer(Score ~ Scale * `Porosity Scale`
                    + (1 | Country/subject_id),
                    # + (1 + `Porosity Scale` | Country), # adding to above, perfectly correlated ranfx
                    control = lmerControl(optimizer = "bobyqa"),
                    data = d4_fortab_long_secular)

r16_secular <- lmer(Score ~ Scale * Absorption
                    + (1 | Country/subject_id),
                    # + (1 + Absorption | Country), # adding to above, perfectly correlated ranfx
                    control = lmerControl(optimizer = "bobyqa"),
                    data = d4_fortab_long_secular)

r17_secular <- lmer(Score ~ Scale * `Porosity Vignettes` * Absorption
                    + (1 | Country/subject_id),
                    # + (1 + `Porosity Vignettes` * Absorption | Country), # adding to above, perfectly correlated ranfx
                    control = lmerControl(optimizer = "bobyqa"),
                    data = d4_fortab_long_secular)

r18_secular <- lmer(Score ~ Scale * `Porosity Scale` * Absorption
                    + (1 | Country/subject_id), 
                    # + (1 + `Porosity Scale` * Absorption | Country), # adding to above, perfectly correlated ranfx
                    control = lmerControl(optimizer = "bobyqa"),
                    data = d4_fortab_long_secular)

r19_secular <- lmer(Score ~ Scale * `Porosity Vignettes` * Absorption
                    + Gender + Age
                    + (1 | Country/subject_id),
                    # + (1 + `Porosity Vignettes` * Absorption + Gender + Age | Country),
                    control = lmerControl(optimizer = "bobyqa"),
                    data = d4_fortab_long_secular)

r20_secular <- lmer(Score ~ Scale * `Porosity Scale` * Absorption
                    + Gender + Age
                    + (1 | Country/subject_id),
                    # + (1 + `Porosity Scale` * Absorption + Gender + Age | Country),
                    control = lmerControl(optimizer = "bobyqa"),
                    data = d4_fortab_long_secular)
table_s49 <- stargazer(r13_secular, r14_secular, #r15_secular,
                       r16_secular, r17_secular, #r18_secular,
                       r19_secular, #r20_secular,
                       type = "html",
                       ci = T,
                       report = c("vcsp"),
                       digits = 2,
                       digits.extra = 1000,
                       omit.table.layout = "n",
                       order = c("^`Porosity Vignettes`$",
                                 "^Absorption$",
                                 "^`Porosity Vignettes`:Absorption$"),
                       # dep.var.labels.include = F,
                       dep.var.caption = "",
                       single.row = T)
cat(sub_interact_fun(table_s49), 
    file = "./stargazer_tables/table_s49.html")
htmltools::includeHTML("./stargazer_tables/table_s49.html")
Score
(1)(2)(3)(4)(5)
Porosity Vignettes0.35 (0.27, 0.42)0.27 (0.20, 0.34)0.27 (0.21, 0.34)
p = 0.00p = 0.00p = 0.00
Absorption0.34 (0.27, 0.40)0.28 (0.22, 0.34)0.27 (0.21, 0.33)
p = 0.00p = 0.00p = 0.00
Porosity Vignettes × Absorption-0.04 (-0.10, 0.02)-0.04 (-0.10, 0.02)
p = 0.16p = 0.23
Scale: Hallucinations0.00 (-0.05, 0.05)0.00 (-0.05, 0.05)0.00 (-0.05, 0.05)0.001 (-0.05, 0.05)-0.002 (-0.06, 0.05)
p = 1.00p = 1.00p = 1.00p = 0.99p = 0.94
Scale: Hallucinations × Porosity Vignettes-0.01 (-0.07, 0.04)-0.02 (-0.07, 0.04)-0.02 (-0.07, 0.04)
p = 0.60p = 0.52p = 0.58
Gender: male-0.05 (-0.11, 0.01)
p = 0.12
Age-0.05 (-0.12, 0.02)
p = 0.16
Scale: Hallucinations × Absorption0.01 (-0.04, 0.07)0.02 (-0.04, 0.07)0.01 (-0.05, 0.06)
p = 0.63p = 0.55p = 0.76
Scale: Hallucinations × Porosity Vignettes × Absorption-0.003 (-0.06, 0.05)-0.0002 (-0.05, 0.05)
p = 0.91p = 1.00
Constant0.004 (-0.16, 0.17)-0.0005 (-0.17, 0.17)0.004 (-0.16, 0.17)0.01 (-0.15, 0.17)-0.01 (-0.16, 0.15)
p = 0.97p = 1.00p = 0.97p = 0.91p = 0.93
Observations1,0101,0101,0101,010980
Log Likelihood-1,408.91-1,373.09-1,363.37-1,344.00-1,304.54
Akaike Inf. Crit.2,827.822,760.182,740.752,710.002,635.09
Bayesian Inf. Crit.2,852.412,794.602,775.172,764.092,698.63
rsq_table_fun(list(r13_secular, r14_secular, #r15_secular,
                   r16_secular, r17_secular, #r18_secular,
                   r19_secular #, r20_secular
))
R-squared Type (1) (2) (3) (4) (5)
Marginal 0.00 0.12 0.11 0.18 0.18
Conditional 0.29 0.31 0.29 0.30 0.28

Table S50. Study 4, measures of secular anomalous events: relationships with Porosity Scale and Absorption (sample as random effect)

table_s50 <- stargazer(r13_secular, #r14_secular, 
                       r15_secular, r16_secular, #r17_secular, 
                       r18_secular, #r19_secular, 
                       r20_secular,
                       type = "html",
                       ci = T,
                       report = c("vcsp"),
                       digits = 2,
                       digits.extra = 1000,
                       omit.table.layout = "n",
                       order = c("^`Porosity Scale`$",
                                 "^Absorption$",
                                 "^`Porosity Scale`:Absorption$"),
                       # dep.var.labels.include = F,
                       dep.var.caption = "",
                       single.row = T)
cat(sub_interact_fun(table_s50), 
    file = "./stargazer_tables/table_s50.html")
htmltools::includeHTML("./stargazer_tables/table_s50.html")
Score
(1)(2)(3)(4)(5)
Porosity Scale0.45 (0.37, 0.53)0.37 (0.29, 0.44)0.35 (0.28, 0.43)
p = 0.00p = 0.00p = 0.00
Absorption0.34 (0.27, 0.40)0.27 (0.21, 0.33)0.26 (0.20, 0.32)
p = 0.00p = 0.00p = 0.00
Porosity Scale × Absorption0.01 (-0.05, 0.06)-0.005 (-0.06, 0.05)
p = 0.80p = 0.87
Scale: Hallucinations0.00 (-0.05, 0.05)-0.00 (-0.05, 0.05)0.00 (-0.05, 0.05)-0.0004 (-0.05, 0.05)-0.003 (-0.06, 0.05)
p = 1.00p = 1.00p = 1.00p = 0.99p = 0.90
Scale: Hallucinations × Porosity Scale-0.07 (-0.12, -0.01)-0.07 (-0.12, -0.02)-0.07 (-0.12, -0.02)
p = 0.02p = 0.01p = 0.02
Gender: male-0.01 (-0.08, 0.05)
p = 0.65
Age-0.06 (-0.13, 0.01)
p = 0.08
Scale: Hallucinations × Absorption0.01 (-0.04, 0.07)0.03 (-0.03, 0.08)0.02 (-0.04, 0.07)
p = 0.63p = 0.34p = 0.50
Scale: Hallucinations × Porosity Scale × Absorption0.002 (-0.05, 0.05)0.001 (-0.05, 0.05)
p = 0.93p = 0.97
Constant0.004 (-0.16, 0.17)-0.005 (-0.26, 0.25)0.004 (-0.16, 0.17)-0.005 (-0.22, 0.21)-0.01 (-0.20, 0.19)
p = 0.97p = 0.97p = 0.97p = 0.97p = 0.94
Observations1,0101,0101,0101,010980
Log Likelihood-1,408.91-1,353.88-1,363.37-1,326.72-1,291.91
Akaike Inf. Crit.2,827.822,721.762,740.752,675.442,609.82
Bayesian Inf. Crit.2,852.412,756.182,775.172,729.532,673.36
rsq_table_fun(list(r13_secular, #r14_secular, 
                   r15_secular, r16_secular, #r17_secular, 
                   r18_secular, #r19_secular 
                   r20_secular
))
R-squared Type (1) (2) (3) (4) (5)
Marginal 0.00 0.18 0.11 0.23 0.22
Conditional 0.29 0.37 0.29 0.35 0.32

Table S51. Study 4, measures of secular anomalous events: relationships with Porosity Vignettes by country

r21_secular_us <- lmer(Score ~ Scale * `Porosity Vignettes` 
                       + (1 | subject_id),
                       control = lmerControl(optimizer = "bobyqa"),
                       data = d4_fortab_long_secular %>% filter(Country == "US") %>%
                         mutate_at(vars(Score, `Porosity Vignettes`), ~ scale(.)))

r21_secular_gh <- lmer(Score ~ Scale * `Porosity Vignettes` 
                       + (1 | subject_id),
                       control = lmerControl(optimizer = "bobyqa"),
                       data = d4_fortab_long_secular %>% filter(Country == "Ghana") %>%
                         mutate_at(vars(Score, ,`Porosity Vignettes`), ~ scale(.)))

r21_secular_th <- lmer(Score ~ Scale * `Porosity Vignettes` 
                       + (1 | subject_id),
                       control = lmerControl(optimizer = "bobyqa"),
                       data = d4_fortab_long_secular %>% filter(Country == "Thailand") %>%
                         mutate_at(vars(Score, `Porosity Vignettes`), ~ scale(.)))

r21_secular_ch <- lmer(Score ~ Scale * `Porosity Vignettes` 
                       + (1 | subject_id),
                       control = lmerControl(optimizer = "bobyqa"),
                       data = d4_fortab_long_secular %>% filter(Country == "China") %>%
                         mutate_at(vars(Score, `Porosity Vignettes`), ~ scale(.)))

r21_secular_vt <- lmer(Score ~ Scale * `Porosity Vignettes` 
                       + (1 | subject_id),
                       control = lmerControl(optimizer = "bobyqa"),
                       data = d4_fortab_long_secular %>% filter(Country == "Vanuatu") %>%
                         mutate_at(vars(Score, `Porosity Vignettes`), ~ scale(.)))
table_s51 <- stargazer(r21_secular_us, r21_secular_gh, r21_secular_th, 
                       r21_secular_ch, r21_secular_vt, 
                       type = "html",
                       ci = T,
                       report = c("vcsp"),
                       digits = 2,
                       digits.extra = 1000,
                       omit.table.layout = "n",
                       order = c("^`Porosity Vignettes`$"),
                       # dep.var.labels.include = F,
                       dep.var.caption = "",
                       single.row = T)
cat(sub_interact_fun(table_s51), 
    file = "./stargazer_tables/table_s51.html")
htmltools::includeHTML("./stargazer_tables/table_s51.html")
Score
(1)(2)(3)(4)(5)
Porosity Vignettes0.53 (0.41, 0.66)0.21 (0.07, 0.35)0.21 (0.06, 0.35)0.39 (0.26, 0.52)0.01 (-0.16, 0.17)
p = 0.00p = 0.003p = 0.005p = 0.00p = 0.93
Scale: Hallucinations0.20 (0.09, 0.30)0.09 (-0.04, 0.22)-0.20 (-0.30, -0.09)-0.08 (-0.19, 0.04)-0.10 (-0.22, 0.02)
p = 0.0002p = 0.18p = 0.0002p = 0.19p = 0.12
Scale: Hallucinations × Porosity Vignettes0.01 (-0.09, 0.11)0.09 (-0.04, 0.22)-0.11 (-0.22, -0.01)-0.24 (-0.35, -0.12)0.04 (-0.08, 0.16)
p = 0.90p = 0.18p = 0.04p = 0.0001p = 0.53
Constant-0.00 (-0.13, 0.13)-0.00 (-0.14, 0.14)-0.00 (-0.14, 0.14)-0.00 (-0.13, 0.13)-0.00 (-0.17, 0.17)
p = 1.00p = 1.00p = 1.00p = 1.00p = 1.00
Observations204200222200184
Log Likelihood-254.85-283.67-305.06-265.61-261.90
Akaike Inf. Crit.521.70579.33622.12543.22535.81
Bayesian Inf. Crit.541.61599.12642.53563.01555.10
rsq_table_fun(list(r21_secular_us, r21_secular_gh, r21_secular_th, 
                   r21_secular_ch, r21_secular_vt))
R-squared Type (1) (2) (3) (4) (5)
Marginal 0.32 0.06 0.09 0.21 0.01
Conditional 0.46 0.12 0.38 0.32 0.31

Table S52. Study 4, measures of secular anomalous events: relationships with Porosity Scale by country

r22_secular_us <- lmer(Score ~ Scale * `Porosity Scale` 
                       + (1 | subject_id),
                       control = lmerControl(optimizer = "bobyqa"),
                       data = d4_fortab_long_secular %>% filter(Country == "US") %>%
                         mutate_at(vars(Score, `Porosity Scale`), ~ scale(.)))

r22_secular_gh <- lmer(Score ~ Scale * `Porosity Scale` 
                       + (1 | subject_id),
                       control = lmerControl(optimizer = "bobyqa"),
                       data = d4_fortab_long_secular %>% filter(Country == "Ghana") %>%
                         mutate_at(vars(Score, `Porosity Scale`), ~ scale(.)))

r22_secular_th <- lmer(Score ~ Scale * `Porosity Scale` 
                       + (1 | subject_id),
                       control = lmerControl(optimizer = "bobyqa"),
                       data = d4_fortab_long_secular %>% filter(Country == "Thailand") %>%
                         mutate_at(vars(Score, `Porosity Scale`), ~ scale(.)))

r22_secular_ch <- lmer(Score ~ Scale * `Porosity Scale` 
                       + (1 | subject_id),
                       control = lmerControl(optimizer = "bobyqa"),
                       data = d4_fortab_long_secular %>% filter(Country == "China") %>%
                         mutate_at(vars(Score, `Porosity Scale`), ~ scale(.)))

r22_secular_vt <- lmer(Score ~ Scale * `Porosity Scale` 
                       + (1 | subject_id),
                       control = lmerControl(optimizer = "bobyqa"),
                       data = d4_fortab_long_secular %>% filter(Country == "Vanuatu") %>%
                         mutate_at(vars(Score, `Porosity Scale`), ~ scale(.)))
table_s52 <- stargazer(r22_secular_us, r22_secular_gh, r22_secular_th, 
                       r22_secular_ch, r22_secular_vt, 
                       type = "html",
                       ci = T,
                       report = c("vcsp"),
                       digits = 2,
                       digits.extra = 1000,
                       omit.table.layout = "n",
                       order = c("^`Porosity Scale`$"),
                       # dep.var.labels.include = F,
                       dep.var.caption = "",
                       single.row = T)
cat(sub_interact_fun(table_s52), 
    file = "./stargazer_tables/table_s52.html")
htmltools::includeHTML("./stargazer_tables/table_s52.html")
Score
(1)(2)(3)(4)(5)
Porosity Scale0.52 (0.40, 0.65)0.16 (0.01, 0.30)0.31 (0.18, 0.45)0.49 (0.37, 0.61)0.23 (0.07, 0.39)
p = 0.00p = 0.04p = 0.00001p = 0.00p = 0.01
Scale: Hallucinations0.20 (0.09, 0.30)0.09 (-0.04, 0.22)-0.20 (-0.30, -0.10)-0.08 (-0.18, 0.03)-0.10 (-0.22, 0.02)
p = 0.0002p = 0.18p = 0.0002p = 0.16p = 0.12
Scale: Hallucinations × Porosity Scale-0.02 (-0.13, 0.08)-0.01 (-0.15, 0.12)-0.18 (-0.28, -0.08)-0.32 (-0.43, -0.22)0.05 (-0.07, 0.18)
p = 0.64p = 0.84p = 0.0005p = 0.00p = 0.38
Constant-0.00 (-0.13, 0.13)-0.00 (-0.14, 0.14)0.00 (-0.14, 0.14)-0.00 (-0.12, 0.12)-0.00 (-0.16, 0.16)
p = 1.00p = 1.00p = 1.00p = 1.00p = 1.00
Observations204200222200184
Log Likelihood-255.66-286.66-296.13-246.83-257.99
Akaike Inf. Crit.523.33585.33604.27505.65527.97
Bayesian Inf. Crit.543.24605.12624.68525.44547.26
rsq_table_fun(list(r22_secular_us, r22_secular_gh, r22_secular_th, 
                   r22_secular_ch, r22_secular_vt))
R-squared Type (1) (2) (3) (4) (5)
Marginal 0.31 0.03 0.17 0.35 0.06
Conditional 0.46 0.11 0.42 0.42 0.32

Table S53. Study 4, measures of secular anomalous events: relationships with Absorption by country

r23_secular_us <- lmer(Score ~ Scale * `Absorption` 
                       + (1 | subject_id),
                       control = lmerControl(optimizer = "bobyqa"),
                       data = d4_fortab_long_secular %>% filter(Country == "US") %>%
                         mutate_at(vars(Score, `Absorption`), ~ scale(.)))

r23_secular_gh <- lmer(Score ~ Scale * `Absorption` 
                       + (1 | subject_id),
                       control = lmerControl(optimizer = "bobyqa"),
                       data = d4_fortab_long_secular %>% filter(Country == "Ghana") %>%
                         mutate_at(vars(Score, `Absorption`), ~ scale(.)))

r23_secular_th <- lmer(Score ~ Scale * `Absorption` 
                       + (1 | subject_id),
                       control = lmerControl(optimizer = "bobyqa"),
                       data = d4_fortab_long_secular %>% filter(Country == "Thailand") %>%
                         mutate_at(vars(Score, `Absorption`), ~ scale(.)))

r23_secular_ch <- lmer(Score ~ Scale * `Absorption` 
                       + (1 | subject_id),
                       control = lmerControl(optimizer = "bobyqa"),
                       data = d4_fortab_long_secular %>% filter(Country == "China") %>%
                         mutate_at(vars(Score, `Absorption`), ~ scale(.)))

r23_secular_vt <- lmer(Score ~ Scale * `Absorption` 
                       + (1 | subject_id),
                       control = lmerControl(optimizer = "bobyqa"),
                       data = d4_fortab_long_secular %>% filter(Country == "Vanuatu") %>%
                         mutate_at(vars(Score, `Absorption`), ~ scale(.)))
table_s53 <- stargazer(r23_secular_us, r23_secular_gh, r23_secular_th, 
                       r23_secular_ch, r23_secular_vt, 
                       type = "html",
                       ci = T,
                       report = c("vcsp"),
                       digits = 2,
                       digits.extra = 1000,
                       omit.table.layout = "n",
                       order = c("^Absorption$"),
                       # dep.var.labels.include = F,
                       dep.var.caption = "",
                       single.row = T)
cat(sub_interact_fun(table_s53), 
    file = "./stargazer_tables/table_s53.html")
htmltools::includeHTML("./stargazer_tables/table_s53.html")
Score
(1)(2)(3)(4)(5)
Absorption0.45 (0.31, 0.59)0.25 (0.11, 0.39)0.38 (0.25, 0.51)0.32 (0.18, 0.46)0.28 (0.12, 0.43)
p = 0.00p = 0.0004p = 0.00000003p = 0.00001p = 0.001
Scale: Hallucinations0.20 (0.09, 0.30)0.09 (-0.04, 0.22)-0.20 (-0.31, -0.09)-0.08 (-0.20, 0.05)-0.10 (-0.22, 0.02)
p = 0.0002p = 0.18p = 0.0003p = 0.23p = 0.12
Scale: Hallucinations × Absorption0.02 (-0.09, 0.12)0.07 (-0.06, 0.20)-0.03 (-0.13, 0.08)0.001 (-0.12, 0.13)0.005 (-0.12, 0.13)
p = 0.76p = 0.33p = 0.64p = 0.99p = 0.95
Constant-0.00 (-0.14, 0.14)-0.00 (-0.14, 0.14)0.00 (-0.13, 0.13)-0.00 (-0.14, 0.14)-0.00 (-0.16, 0.16)
p = 1.00p = 1.00p = 1.00p = 1.00p = 1.00
Observations204200222200184
Log Likelihood-263.58-282.35-297.18-278.38-256.45
Akaike Inf. Crit.539.16576.69606.37568.76524.89
Bayesian Inf. Crit.559.06596.48626.78588.55544.18
rsq_table_fun(list(r23_secular_us, r23_secular_gh, r23_secular_th, 
                   r23_secular_ch, r23_secular_vt))
R-squared Type (1) (2) (3) (4) (5)
Marginal 0.24 0.08 0.18 0.11 0.08
Conditional 0.46 0.11 0.35 0.21 0.31

Exploratory factor analyses

Study 1

All sites

d1_abs <- d1_byq %>%
  select(subject_id, ends_with("_abs")) %>%
  column_to_rownames("subject_id")
fa.parallel(d1_abs)
Parallel analysis suggests that the number of factors =  4  and the number of components =  3 

d1_all_efa <- fa(d1_abs, nfactors = 4, rotate = "oblimin")
Loading required namespace: GPArotation
d1_all_loadings <- d1_all_efa$loadings[] %>% 
  fa.sort() %>%
  data.frame() %>%
  rownames_to_column("question") %>%
  mutate(order = 1:nrow(.)) %>%
  gather(factor, loading, -question, -order)
d1_all_loadings %>%
  mutate(question = gsub("_abs", "", question)) %>%
  ggplot(aes(x = factor, y = reorder(question, desc(order)), fill = loading)) +
  geom_tile(color = "black") +
  geom_text(aes(label = format(round(loading, 2), nsmall = 2)), size = 3) +
  scale_fill_distiller(palette = "RdYlBu", limits = c(-1, 1)) +
  labs(title = "Study 1: All sites", x = "factor", y = "question")

US

d1_abs_us <- d1_byq %>%
  filter(country == "US") %>%
  select(subject_id, ends_with("_abs")) %>%
  column_to_rownames("subject_id")
fa.parallel(d1_abs_us)
Matrix was not positive definite, smoothing was doneMatrix was not positive definite, smoothing was doneMatrix was not positive definite, smoothing was doneMatrix was not positive definite, smoothing was doneMatrix was not positive definite, smoothing was doneMatrix was not positive definite, smoothing was doneThe estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.Matrix was not positive definite, smoothing was doneMatrix was not positive definite, smoothing was doneMatrix was not positive definite, smoothing was doneThe estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.Matrix was not positive definite, smoothing was doneMatrix was not positive definite, smoothing was doneMatrix was not positive definite, smoothing was doneThe estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.Matrix was not positive definite, smoothing was doneMatrix was not positive definite, smoothing was doneMatrix was not positive definite, smoothing was doneThe estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.Matrix was not positive definite, smoothing was doneMatrix was not positive definite, smoothing was doneMatrix was not positive definite, smoothing was doneThe estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.NaNs produceddiag(.) had 0 or NA entries; non-finite result is doubtful
Parallel analysis suggests that the number of factors =  3  and the number of components =  1 

d1_us_efa <- fa(d1_abs, nfactors = 3, rotate = "oblimin")
d1_us_loadings <- d1_us_efa$loadings[] %>% 
  fa.sort() %>%
  data.frame() %>%
  rownames_to_column("question") %>%
  mutate(order = 1:nrow(.)) %>%
  gather(factor, loading, -question, -order)
d1_us_loadings %>%
  mutate(question = gsub("_abs", "", question)) %>%
  ggplot(aes(x = factor, y = reorder(question, desc(order)), fill = loading)) +
  geom_tile(color = "black") +
  geom_text(aes(label = format(round(loading, 2), nsmall = 2)), size = 3) +
  scale_fill_distiller(palette = "RdYlBu", limits = c(-1, 1)) +
  labs(title = "Study 1: US", x = "factor", y = "question")

Ghana

d1_abs_gh <- d1_byq %>%
  filter(country == "Ghana") %>%
  select(subject_id, ends_with("_abs")) %>%
  column_to_rownames("subject_id")
fa.parallel(d1_abs_gh)
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 

d1_gh_efa <- fa(d1_abs, nfactors = 2, rotate = "oblimin")
d1_gh_loadings <- d1_gh_efa$loadings[] %>% 
  fa.sort() %>%
  data.frame() %>%
  rownames_to_column("question") %>%
  mutate(order = 1:nrow(.)) %>%
  gather(factor, loading, -question, -order)
d1_gh_loadings %>%
  mutate(question = gsub("_abs", "", question)) %>%
  ggplot(aes(x = factor, y = reorder(question, desc(order)), fill = loading)) +
  geom_tile(color = "black") +
  geom_text(aes(label = format(round(loading, 2), nsmall = 2)), size = 3) +
  scale_fill_distiller(palette = "RdYlBu", limits = c(-1, 1)) +
  labs(title = "Study 1: Ghana", x = "factor", y = "question")

Thailand

d1_abs_th <- d1_byq %>%
  filter(country == "Thailand") %>%
  select(subject_id, ends_with("_abs")) %>%
  column_to_rownames("subject_id")
fa.parallel(d1_abs_th)
Matrix was not positive definite, smoothing was doneMatrix was not positive definite, smoothing was doneMatrix was not positive definite, smoothing was doneThe estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.Matrix was not positive definite, smoothing was doneMatrix was not positive definite, smoothing was doneMatrix was not positive definite, smoothing was doneThe estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.
Parallel analysis suggests that the number of factors =  1  and the number of components =  1 

d1_th_efa <- fa(d1_abs, nfactors = 1, rotate = "oblimin")
d1_th_loadings <- d1_th_efa$loadings[] %>% 
  fa.sort() %>%
  data.frame() %>%
  rownames_to_column("question") %>%
  mutate(order = 1:nrow(.)) %>%
  gather(factor, loading, -question, -order)
d1_th_loadings %>%
  mutate(question = gsub("_abs", "", question)) %>%
  ggplot(aes(x = factor, y = reorder(question, desc(order)), fill = loading)) +
  geom_tile(color = "black") +
  geom_text(aes(label = format(round(loading, 2), nsmall = 2)), size = 3) +
  scale_fill_distiller(palette = "RdYlBu", limits = c(-1, 1)) +
  labs(title = "Study 1: Thailand", x = "factor", y = "question")

China

d1_abs_ch <- d1_byq %>%
  filter(country == "China") %>%
  select(subject_id, ends_with("_abs")) %>%
  column_to_rownames("subject_id")
fa.parallel(d1_abs_ch)
The estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.Matrix was not positive definite, smoothing was doneMatrix was not positive definite, smoothing was doneMatrix was not positive definite, smoothing was doneThe estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.Matrix was not positive definite, smoothing was doneMatrix was not positive definite, smoothing was doneMatrix was not positive definite, smoothing was doneThe estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.Matrix was not positive definite, smoothing was doneMatrix was not positive definite, smoothing was doneMatrix was not positive definite, smoothing was doneThe estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.
Parallel analysis suggests that the number of factors =  1  and the number of components =  1 

d1_ch_efa <- fa(d1_abs, nfactors = 1, rotate = "oblimin")
d1_ch_loadings <- d1_ch_efa$loadings[] %>% 
  fa.sort() %>%
  data.frame() %>%
  rownames_to_column("question") %>%
  mutate(order = 1:nrow(.)) %>%
  gather(factor, loading, -question, -order)
d1_ch_loadings %>%
  mutate(question = gsub("_abs", "", question)) %>%
  ggplot(aes(x = factor, y = reorder(question, desc(order)), fill = loading)) +
  geom_tile(color = "black") +
  geom_text(aes(label = format(round(loading, 2), nsmall = 2)), size = 3) +
  scale_fill_distiller(palette = "RdYlBu", limits = c(-1, 1)) +
  labs(title = "Study 1: China", x = "factor", y = "question")

Vanuatu

d1_abs_vt <- d1_byq %>%
  filter(country == "Vanuatu") %>%
  select(subject_id, ends_with("_abs")) %>%
  column_to_rownames("subject_id")
fa.parallel(d1_abs_vt)
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Try a different factor score estimation method.the standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation 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Matrix was not positive definite, smoothing was doneIn smc, smcs < 0 were set to .0
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Try a different factor score estimation method.the standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zeroMatrix was not positive definite, smoothing was donethe standard deviation is zeroIn smc, smcs < 0 were set to .0
Matrix was not positive definite, smoothing was donethe standard deviation is zeroIn smc, smcs < 0 were set to .0
the standard deviation is zerothe standard deviation is zerothe standard deviation is zeroMatrix was not positive definite, smoothing was doneThe estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.the standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zeroMatrix was not positive definite, smoothing was donethe standard deviation is zeroIn smc, smcs < 0 were set to .0
the standard deviation is zeroMatrix was not positive definite, smoothing was doneIn smc, smcs < 0 were set to .0
the standard deviation is zerothe standard deviation is zerothe standard deviation is zeroMatrix was not positive definite, smoothing was donethe standard deviation is zeroThe estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.the standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zeroMatrix was not positive definite, smoothing was donethe standard deviation is zeroMatrix was not positive definite, smoothing was donethe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zeroMatrix was not positive definite, smoothing was donethe standard deviation is zeroThe estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.the standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zeroMatrix was not positive definite, smoothing was donethe standard deviation is zeroIn smc, smcs < 0 were set to .0
Matrix was not positive definite, smoothing was donethe standard deviation is zeroIn smc, smcs < 0 were set to .0
the standard deviation is zerothe standard deviation is zeroMatrix was not positive definite, smoothing was doneThe estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.the standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zeroMatrix was not positive definite, smoothing was donethe standard deviation is zeroIn smc, smcs < 0 were set to .0
Matrix was not positive definite, smoothing was doneIn smc, smcs < 0 were set to .0
the standard deviation is zerothe standard deviation is zerothe standard deviation is zeroMatrix was not positive definite, smoothing was doneThe estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.the standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zeroMatrix was not positive definite, smoothing was doneIn smc, smcs < 0 were set to .0
the standard deviation is zeroMatrix was not positive definite, smoothing was doneIn smc, smcs < 0 were set to .0
the standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zeroMatrix was not positive definite, smoothing was donethe standard deviation is zeroThe estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.the standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zeroMatrix was not positive definite, smoothing was donethe standard deviation is zeroIn smc, smcs < 0 were set to .0
Matrix was not positive definite, smoothing was doneIn smc, smcs < 0 were set to .0
the standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zeroMatrix was not positive definite, smoothing was donethe standard deviation is zeroThe estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.the standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zeroMatrix was not positive definite, smoothing was doneIn smc, smcs < 0 were set to .0
Matrix was not positive definite, smoothing was donethe standard deviation is zeroIn smc, smcs < 0 were set to .0
the standard deviation is zerothe standard deviation is zeroMatrix was not positive definite, smoothing was doneThe estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.the standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zeroMatrix was not positive definite, smoothing was doneIn smc, smcs < 0 were set to .0
Matrix was not positive definite, smoothing was donethe standard deviation is zeroIn smc, smcs < 0 were set to .0
the standard deviation is zerothe standard deviation is zeroMatrix was not positive definite, smoothing was doneThe estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.the standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zeroMatrix was not positive definite, smoothing was donethe standard deviation is zeroMatrix was not positive definite, smoothing was donethe standard deviation is zerothe standard deviation is zerothe standard deviation is zeroMatrix was not positive definite, smoothing was donethe standard deviation is zeroThe estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.the standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zeroMatrix was not positive definite, smoothing was doneMatrix was not positive definite, smoothing was donethe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zeroMatrix was not positive definite, smoothing was doneThe estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.the standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zeroMatrix was not positive definite, smoothing was doneMatrix was not positive definite, smoothing was donethe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zeroMatrix was not positive definite, smoothing was doneThe estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.the standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zeroMatrix was not positive definite, smoothing was doneIn smc, smcs < 0 were set to .0
Matrix was not positive definite, smoothing was donethe standard deviation is zeroIn smc, smcs < 0 were set to .0
the standard deviation is zerothe standard deviation is zerothe standard deviation is zeroMatrix was not positive definite, smoothing was donethe standard deviation is zeroThe estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.the standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zeroMatrix was not positive definite, smoothing was doneMatrix was not positive definite, smoothing was doneMatrix was not positive definite, smoothing was doneThe estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.the standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zeroMatrix was not positive definite, smoothing was doneMatrix was not positive definite, smoothing was doneMatrix was not positive definite, smoothing was doneThe estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.the standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zeroMatrix was not positive definite, smoothing was doneIn smc, smcs < 0 were set to .0
Matrix was not positive definite, smoothing was doneIn smc, smcs < 0 were set to .0
Matrix was not positive definite, smoothing was doneThe estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.the standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zerothe standard deviation is zeroMatrix was not positive definite, smoothing was doneMatrix was not positive definite, smoothing was doneMatrix was not positive definite, smoothing was doneThe estimated weights for the factor scores are probably incorrect.  Try a different factor score estimation method.
Parallel analysis suggests that the number of factors =  5  and the number of components =  4 

d1_vt_efa <- fa(d1_abs, nfactors = 5, rotate = "oblimin")
d1_vt_loadings <- d1_vt_efa$loadings[] %>% 
  fa.sort() %>%
  data.frame() %>%
  rownames_to_column("question") %>%
  mutate(order = 1:nrow(.)) %>%
  gather(factor, loading, -question, -order)
d1_vt_loadings %>%
  mutate(question = gsub("_abs", "", question)) %>%
  ggplot(aes(x = factor, y = reorder(question, desc(order)), fill = loading)) +
  geom_tile(color = "black") +
  geom_text(aes(label = format(round(loading, 2), nsmall = 2)), size = 3) +
  scale_fill_distiller(palette = "RdYlBu", limits = c(-1, 1)) +
  labs(title = "Study 1: Vanuatu", x = "factor", y = "question")

Study 3

All sites

d3_abs <- d3_byq %>%
  select(subj, question, response) %>%
  filter(question %in% c("exwl_01", "exwl_02", "exwl_03", "exwl_04", "exwl_05", "exwl_06",
                         "exwl_07", "exwl_08", "exwl_09", "exwl_10", "exwl_11", "exwl_12",
                         "exwl_13", "exwl_14", "exwl_15", "exwl_16", "exwl_17", "exwl_18",
                         "exwl_19", "exwl_20", "exwl_21", "exwl_22", "exwl_23", "exwl_24",
                         "exwl_25", "exwl_26", "exwl_27", "exwl_28", "exwl_29", "exwl_30",
                         "exwl_31", "exwl_32", "exwl_33", "exwl_34")) %>%
  mutate(question = factor(
    question,
    levels = c("exwl_01", "exwl_02", "exwl_03", "exwl_04", 
               "exwl_05", "exwl_06", "exwl_07", "exwl_08", 
               "exwl_09", "exwl_10", "exwl_11", "exwl_12",
               "exwl_13", "exwl_14", "exwl_15", "exwl_16", 
               "exwl_17", "exwl_18", "exwl_19", "exwl_20", 
               "exwl_21", "exwl_22", "exwl_23", "exwl_24",
               "exwl_25", "exwl_26", "exwl_27", "exwl_28", 
               "exwl_29", "exwl_30", "exwl_31", "exwl_32", 
               "exwl_33", "exwl_34"),
    labels = c("childexp", "poeticlanguage", "forgetsurrounding", "starepicture", 
               "mindenvelopworld", "cloudshapes", "vividdaydream", "mysticalexp", 
               "stepoutsidemyself", "textures", "doublyreal", "caughtupinmusic", 
               "heavybody", "sensepresence", "woodfireimagine", "immersednature", 
               "colormeaning", "wanderthoughtstask", "clearpastexp", "meaninglesstoothers", 
               "actinginplay", "visualthoughts", "delightsmallthings", "organmusic", 
               "changenoise", "vividsmellmemories", "synesthesiasound", "predictwords", 
               "physicalmemories", "fascinatingvoice", "invisiblepresence", "sponthoughtsimages", 
               "synesthesiasmell", "emosunset"))) %>%
  spread(question, response) %>%
  column_to_rownames("subj")
fa.parallel(d3_abs)
Parallel analysis suggests that the number of factors =  5  and the number of components =  4 

d3_all_efa <- fa(d3_abs, nfactors = 6, rotate = "oblimin")
d3_all_loadings <- d3_all_efa$loadings[] %>% 
  fa.sort() %>%
  data.frame() %>%
  rownames_to_column("question") %>%
  mutate(order = 1:nrow(.)) %>%
  gather(factor, loading, -question, -order)
d3_all_loadings %>%
  mutate(question = gsub("_abs", "", question)) %>%
  ggplot(aes(x = factor, y = reorder(question, desc(order)), fill = loading)) +
  geom_tile(color = "black") +
  geom_text(aes(label = format(round(loading, 2), nsmall = 2)), size = 3) +
  scale_fill_distiller(palette = "RdYlBu", limits = c(-1, 1)) +
  labs(title = "Study 3: All sites", x = "factor", y = "question")

US

d3_abs_us <- d3_abs %>%
  rownames_to_column("subj") %>%
  filter(subj %in% c(d3_byq %>% filter(ctry == "us") %>% distinct(subj) %>% pull(subj))) %>%
  column_to_rownames("subj")
fa.parallel(d3_abs_us)
Parallel analysis suggests that the number of factors =  4  and the number of components =  3 

d3_us_efa <- fa(d3_abs, nfactors = 4, rotate = "oblimin")
d3_us_loadings <- d3_us_efa$loadings[] %>% 
  fa.sort() %>%
  data.frame() %>%
  rownames_to_column("question") %>%
  mutate(order = 1:nrow(.)) %>%
  gather(factor, loading, -question, -order)
d3_us_loadings %>%
  mutate(question = gsub("_abs", "", question)) %>%
  ggplot(aes(x = factor, y = reorder(question, desc(order)), fill = loading)) +
  geom_tile(color = "black") +
  geom_text(aes(label = format(round(loading, 2), nsmall = 2)), size = 3) +
  scale_fill_distiller(palette = "RdYlBu", limits = c(-1, 1)) +
  labs(title = "Study 3: US", x = "factor", y = "question")

Ghana

d3_abs_gh <- d3_abs %>%
  rownames_to_column("subj") %>%
  filter(subj %in% c(d3_byq %>% filter(ctry == "ghana") %>% distinct(subj) %>% pull(subj))) %>%
  column_to_rownames("subj")
fa.parallel(d3_abs_gh)
Parallel analysis suggests that the number of factors =  2  and the number of components =  1 

d3_gh_efa <- fa(d3_abs, nfactors = 2, rotate = "oblimin")
d3_gh_loadings <- d3_gh_efa$loadings[] %>% 
  fa.sort() %>%
  data.frame() %>%
  rownames_to_column("question") %>%
  mutate(order = 1:nrow(.)) %>%
  gather(factor, loading, -question, -order)
d3_gh_loadings %>%
  mutate(question = gsub("_abs", "", question)) %>%
  ggplot(aes(x = factor, y = reorder(question, desc(order)), fill = loading)) +
  geom_tile(color = "black") +
  geom_text(aes(label = format(round(loading, 2), nsmall = 2)), size = 3) +
  scale_fill_distiller(palette = "RdYlBu", limits = c(-1, 1)) +
  labs(title = "Study 3: Ghana", x = "factor", y = "question")

Thailand

d3_abs_th <- d3_abs %>%
  rownames_to_column("subj") %>%
  filter(subj %in% c(d3_byq %>% filter(ctry == "thailand") %>% distinct(subj) %>% pull(subj))) %>%
  column_to_rownames("subj")
fa.parallel(d3_abs_th)
Parallel analysis suggests that the number of factors =  1  and the number of components =  1 

d3_th_efa <- fa(d3_abs, nfactors = 1, rotate = "oblimin")
d3_th_loadings <- d3_th_efa$loadings[] %>% 
  fa.sort() %>%
  data.frame() %>%
  rownames_to_column("question") %>%
  mutate(order = 1:nrow(.)) %>%
  gather(factor, loading, -question, -order)
d3_th_loadings %>%
  mutate(question = gsub("_abs", "", question)) %>%
  ggplot(aes(x = factor, y = reorder(question, desc(order)), fill = loading)) +
  geom_tile(color = "black") +
  geom_text(aes(label = format(round(loading, 2), nsmall = 2)), size = 3) +
  scale_fill_distiller(palette = "RdYlBu", limits = c(-1, 1)) +
  labs(title = "Study 3: Thailand", x = "factor", y = "question")

China

d3_abs_ch <- d3_abs %>%
  rownames_to_column("subj") %>%
  filter(subj %in% c(d3_byq %>% filter(ctry == "china") %>% distinct(subj) %>% pull(subj))) %>%
  column_to_rownames("subj")
fa.parallel(d3_abs_ch)
the standard deviation is zerothe standard deviation is zerothe standard deviation is zero
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 

d3_ch_efa <- fa(d3_abs, nfactors = 2, rotate = "oblimin")
d3_ch_loadings <- d3_ch_efa$loadings[] %>% 
  fa.sort() %>%
  data.frame() %>%
  rownames_to_column("question") %>%
  mutate(order = 1:nrow(.)) %>%
  gather(factor, loading, -question, -order)
d3_ch_loadings %>%
  mutate(question = gsub("_abs", "", question)) %>%
  ggplot(aes(x = factor, y = reorder(question, desc(order)), fill = loading)) +
  geom_tile(color = "black") +
  geom_text(aes(label = format(round(loading, 2), nsmall = 2)), size = 3) +
  scale_fill_distiller(palette = "RdYlBu", limits = c(-1, 1)) +
  labs(title = "Study 3: China", x = "factor", y = "question")

Vanuatu

d3_abs_vt <- d3_abs %>%
  rownames_to_column("subj") %>%
  filter(subj %in% c(d3_byq %>% filter(ctry == "vanuatu") %>% distinct(subj) %>% pull(subj))) %>%
  column_to_rownames("subj")
fa.parallel(d3_abs_vt)
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 

d3_vt_efa <- fa(d3_abs, nfactors = 2, rotate = "oblimin")
d3_vt_loadings <- d3_vt_efa$loadings[] %>% 
  fa.sort() %>%
  data.frame() %>%
  rownames_to_column("question") %>%
  mutate(order = 1:nrow(.)) %>%
  gather(factor, loading, -question, -order)
d3_vt_loadings %>%
  mutate(question = gsub("_abs", "", question)) %>%
  ggplot(aes(x = factor, y = reorder(question, desc(order)), fill = loading)) +
  geom_tile(color = "black") +
  geom_text(aes(label = format(round(loading, 2), nsmall = 2)), size = 3) +
  scale_fill_distiller(palette = "RdYlBu", limits = c(-1, 1)) +
  labs(title = "Study 3: Vanuatu", x = "factor", y = "question")

Study 4

All sites

d4_abs <- d4_byq %>%
  select(p7_subj, contains("_abs_")) %>%
  select(-ends_with("cat"), -ends_with("total"), -contains("_check")) %>%
  column_to_rownames("p7_subj")
fa.parallel(d4_abs)
Parallel analysis suggests that the number of factors =  4  and the number of components =  3 

d4_all_efa <- fa(d4_abs, nfactors = 4, rotate = "oblimin")
d4_all_loadings <- d4_all_efa$loadings[] %>% 
  fa.sort() %>%
  data.frame() %>%
  rownames_to_column("question") %>%
  mutate(order = 1:nrow(.)) %>%
  gather(factor, loading, -question, -order)
d4_all_loadings %>%
  mutate(question = gsub("_abs", "", question),
         question = gsub("p7_", "", question)) %>%
  ggplot(aes(x = factor, y = reorder(question, desc(order)), fill = loading)) +
  geom_tile(color = "black") +
  geom_text(aes(label = format(round(loading, 2), nsmall = 2)), size = 3) +
  scale_fill_distiller(palette = "RdYlBu", limits = c(-1, 1)) +
  labs(title = "Study 4: All sites", x = "factor", y = "question")

US

d4_abs_us <- d4_abs %>%
  rownames_to_column("p7_subj") %>%
  filter(p7_subj %in% c(d4_byq %>% 
                          filter(p7_ctry == "US") %>% 
                          distinct(p7_subj) %>% 
                          pull(p7_subj))) %>%
  column_to_rownames("p7_subj")
fa.parallel(d4_abs_us)
Parallel analysis suggests that the number of factors =  2  and the number of components =  2 
There were 45 warnings (use warnings() to see them)

d4_us_efa <- fa(d4_abs_us, nfactors = 2, rotate = "oblimin")
d4_us_loadings <- d4_us_efa$loadings[] %>% 
  fa.sort() %>%
  data.frame() %>%
  rownames_to_column("question") %>%
  mutate(order = 1:nrow(.)) %>%
  gather(factor, loading, -question, -order)
d4_us_loadings %>%
  mutate(question = gsub("_abs", "", question),
         question = gsub("p7_", "", question)) %>%
  ggplot(aes(x = factor, y = reorder(question, desc(order)), fill = loading)) +
  geom_tile(color = "black") +
  geom_text(aes(label = format(round(loading, 2), nsmall = 2)), size = 3) +
  scale_fill_distiller(palette = "RdYlBu", limits = c(-1, 1)) +
  labs(title = "Study 4: US", x = "factor", y = "question")

Ghana

d4_abs_gh <- d4_abs %>%
  rownames_to_column("p7_subj") %>%
  filter(p7_subj %in% c(d4_byq %>% 
                          filter(p7_ctry == "Ghana") %>% 
                          distinct(p7_subj) %>% 
                          pull(p7_subj))) %>%
  column_to_rownames("p7_subj")
fa.parallel(d4_abs_gh)
Parallel analysis suggests that the number of factors =  4  and the number of components =  1 

d4_gh_efa <- fa(d4_abs_gh, nfactors = 4, rotate = "oblimin")
d4_gh_loadings <- d4_gh_efa$loadings[] %>% 
  fa.sort() %>%
  data.frame() %>%
  rownames_to_column("question") %>%
  mutate(order = 1:nrow(.)) %>%
  gather(factor, loading, -question, -order)
d4_gh_loadings %>%
  mutate(question = gsub("_abs", "", question),
         question = gsub("p7_", "", question)) %>%
  ggplot(aes(x = factor, y = reorder(question, desc(order)), fill = loading)) +
  geom_tile(color = "black") +
  geom_text(aes(label = format(round(loading, 2), nsmall = 2)), size = 3) +
  scale_fill_distiller(palette = "RdYlBu", limits = c(-1, 1)) +
  labs(title = "Study 4: Ghana", x = "factor", y = "question")

Thailand

d4_abs_th <- d4_abs %>%
  rownames_to_column("p7_subj") %>%
  filter(p7_subj %in% c(d4_byq %>% 
                          filter(p7_ctry == "Thailand") %>% 
                          distinct(p7_subj) %>% 
                          pull(p7_subj))) %>%
  column_to_rownames("p7_subj")
fa.parallel(d4_abs_th)
the standard deviation is zero
Parallel analysis suggests that the number of factors =  1  and the number of components =  1 

d4_th_efa <- fa(d4_abs_th, nfactors = 1, rotate = "oblimin")
d4_th_loadings <- d4_th_efa$loadings[] %>% 
  fa.sort() %>%
  data.frame() %>%
  rownames_to_column("question") %>%
  mutate(order = 1:nrow(.)) %>%
  gather(factor, loading, -question, -order)
d4_th_loadings %>%
  mutate(question = gsub("_abs", "", question)) %>%
  ggplot(aes(x = factor, y = reorder(question, desc(order)), fill = loading)) +
  geom_tile(color = "black") +
  geom_text(aes(label = format(round(loading, 2), nsmall = 2)), size = 3) +
  scale_fill_distiller(palette = "RdYlBu", limits = c(-1, 1)) +
  labs(title = "Study 4: Thailand", x = "factor", y = "question")

China

d4_abs_ch <- d4_abs %>%
  rownames_to_column("p7_subj") %>%
  filter(p7_subj %in% c(d4_byq %>% 
                          filter(p7_ctry == "China") %>% 
                          distinct(p7_subj) %>% 
                          pull(p7_subj))) %>%
  column_to_rownames("p7_subj")
fa.parallel(d4_abs_ch)
the standard deviation is zerothe standard deviation is zerothe standard deviation is zero
Parallel analysis suggests that the number of factors =  1  and the number of components =  1 

d4_ch_efa <- fa(d4_abs_ch, nfactors = 1, rotate = "oblimin")
d4_ch_loadings <- d4_ch_efa$loadings[] %>% 
  fa.sort() %>%
  data.frame() %>%
  rownames_to_column("question") %>%
  mutate(order = 1:nrow(.)) %>%
  gather(factor, loading, -question, -order)
d4_ch_loadings %>%
  mutate(question = gsub("_abs", "", question),
         question = gsub("p7_", "", question)) %>%
  ggplot(aes(x = factor, y = reorder(question, desc(order)), fill = loading)) +
  geom_tile(color = "black") +
  geom_text(aes(label = format(round(loading, 2), nsmall = 2)), size = 3) +
  scale_fill_distiller(palette = "RdYlBu", limits = c(-1, 1)) +
  labs(title = "Study 4: China", x = "factor", y = "question")

Vanuatu

d4_abs_vt <- d4_abs %>%
  rownames_to_column("p7_subj") %>%
  filter(p7_subj %in% c(d4_byq %>% 
                          filter(p7_ctry == "Vanuatu") %>% 
                          distinct(p7_subj) %>% 
                          pull(p7_subj))) %>%
  column_to_rownames("p7_subj")
fa.parallel(d4_abs_vt)
the standard deviation is zero
Parallel analysis suggests that the number of factors =  6  and the number of components =  1 

d4_vt_efa <- fa(d4_abs_vt, nfactors = 6, rotate = "oblimin")
d4_vt_loadings <- d4_vt_efa$loadings[] %>% 
  fa.sort() %>%
  data.frame() %>%
  rownames_to_column("question") %>%
  mutate(order = 1:nrow(.)) %>%
  gather(factor, loading, -question, -order)
d4_vt_loadings %>%
  mutate(question = gsub("_abs", "", question),
         question = gsub("p7_", "", question)) %>%
  ggplot(aes(x = factor, y = reorder(question, desc(order)), fill = loading)) +
  geom_tile(color = "black") +
  geom_text(aes(label = format(round(loading, 2), nsmall = 2)), size = 3) +
  scale_fill_distiller(palette = "RdYlBu", limits = c(-1, 1)) +
  labs(title = "Study 4: Vanuatu", x = "factor", y = "question")

All studies

d_all_abs <- d1_abs %>% 
  rename_all(~gsub("_abs", "", .)) %>%
  rownames_to_column("subject_id") %>%
  mutate(study = "s1") %>%
  gather(question, response, -c(study, subject_id)) %>%
  full_join(d3_abs %>%
              rownames_to_column("subject_id") %>%
              mutate(study = "s3") %>%
              gather(question, response, -c(study, subject_id))) %>%
  full_join(d4_abs %>%
              rownames_to_column("subject_id") %>%
              mutate(study = "s4") %>%
              gather(question, response, -c(study, subject_id)) %>%
              mutate(question = recode(question,
                                       "p7_abs_child.exp" = "childexp",
                                       "p7_abs_poetic" = "poeticlanguage", 
                                       "p7_abs_tv.real" = "forgetsurrounding",
                                       "p7_abs_see.image" = "starepicture",
                                       "p7_abs_mind.world" = "mindenvelopworld", 
                                       "p7_abs_clouds" = "cloudshapes", 
                                       "p7_abs_vivid.dreams" = "vividdaydream",
                                       "p7_abs_mystic.exp" = "mysticalexp", 
                                       "p7_abs_step.outside" = "stepoutsidemyself", 
                                       "p7_abs_textures" = "textures", 
                                       "p7_abs_too.real" = "doublyreal", 
                                       "p7_abs_music.attn" = "caughtupinmusic", 
                                       "p7_abs_heavy.body" = "heavybody",
                                       "p7_abs_sense.presc" = "sensepresence",
                                       "p7_abs_fire" = "woodfireimagine", 
                                       "p7_abs_nature.art" = "immersednature", 
                                       "p7_abs_colors" = "colormeaning", 
                                       "p7_abs_thght.wander" = "wanderthoughtstask", 
                                       "p7_abs_vivid.past" = "clearpastexp", 
                                       "p7_abs_makes.sense" = "meaninglesstoothers", 
                                       "p7_abs_become.chctr" = "actinginplay", 
                                       "p7_abs_visual.thghts" = "visualthoughts", 
                                       "p7_abs_small.things" = "delightsmallthings", 
                                       "p7_abs_music.lift" = "organmusic", 
                                       "p7_abs_noise.music" = "changenoise", 
                                       "p7_abs_scented.mem" = "vividsmellmemories", 
                                       "p7_abs_visual.music" = "synesthesiasound", 
                                       "p7_abs_before.said" = "predictwords", 
                                       "p7_abs_physical.mem" = "physicalmemories", 
                                       "p7_abs_voice.sound" = "fascinatingvoice", 
                                       "p7_abs_not.physical" = "invisiblepresence",
                                       "p7_abs_thgts.image" = "sponthoughtsimages", 
                                       "p7_abs_odor.to.color" = "synesthesiasmell",
                                       "p7_abs_sunset" = "emosunset"))) %>%
  unite("study_sub", c(study, subject_id)) %>%
  spread(question, response) %>%
  column_to_rownames("study_sub")
Joining, by = c("subject_id", "study", "question", "response")
Joining, by = c("subject_id", "study", "question", "response")
fa.parallel(d_all_abs)
Parallel analysis suggests that the number of factors =  6  and the number of components =  4 

d_all_all_efa <- fa(d_all_abs, nfactors = 6, rotate = "oblimin")
d_all_all_loadings <- d_all_all_efa$loadings[] %>% 
  fa.sort() %>%
  data.frame() %>%
  rownames_to_column("question") %>%
  mutate(order = 1:nrow(.)) %>%
  gather(factor, loading, -question, -order)
d_all_all_loadings %>%
  ggplot(aes(x = factor, y = reorder(question, desc(order)), fill = loading)) +
  geom_tile(color = "black") +
  geom_text(aes(label = format(round(loading, 2), nsmall = 2)), size = 3) +
  scale_fill_distiller(palette = "RdYlBu", limits = c(-1, 1)) +
  labs(title = "All studies: All sites", x = "factor", y = "question")

d_all_all_scales <- d_all_all_loadings %>%
  group_by(factor) %>% 
  top_n(3, loading) %>%
  ungroup()
d_all_all_loadings %>%
  ggplot(aes(x = factor, y = reorder(question, desc(order)), fill = loading)) +
  geom_tile(color = "black") +
  geom_tile(data = d_all_all_scales %>%
              mutate(factorlab = recode_factor(factor,
                                               "MR1" = "Synesthesia",
                                               "MR2" = "Aesthetic involvement in nature",
                                               "MR3" = "Altered states of consciousness (a)",
                                               "MR4" = "Imaginative involvement",
                                               "MR5" = "Sensed presence",
                                               "MR6" = "Altered states of consciousness (b)")),
            aes(fill = NULL, color = factorlab), alpha = 0, size = 1) +
  geom_text(aes(label = format(round(loading, 2), nsmall = 2)), size = 3) +
  scale_fill_distiller(palette = "RdYlBu", limits = c(-1, 1)) +
  scale_color_brewer(palette = "Set1") +
  labs(title = "All studies: All sites", x = "factor", y = "question",
       fill = "Factor loading", color = "MODTAS factor") +
  theme_minimal()

Subscales

Study 1

Reliability

# reliability
bind_rows(MR1 = summary(alpha(d_all_abs %>%
                                rownames_to_column("subject_id") %>% 
                                filter(grepl("s1_", subject_id)) %>%
                                column_to_rownames("subject_id") %>%
                                select(d_all_all_scales %>% 
                                         filter(factor == "MR1") %>% 
                                         pull(question)))),
          MR2 = summary(alpha(d_all_abs %>%
                                rownames_to_column("subject_id") %>% 
                                filter(grepl("s1_", subject_id)) %>%
                                column_to_rownames("subject_id") %>%
                                select(d_all_all_scales %>% 
                                         filter(factor == "MR2") %>% 
                                         pull(question)))),
          MR3 = summary(alpha(d_all_abs %>%
                                rownames_to_column("subject_id") %>% 
                                filter(grepl("s1_", subject_id)) %>%
                                column_to_rownames("subject_id") %>%
                                select(d_all_all_scales %>% 
                                         filter(factor == "MR3") %>% 
                                         pull(question)))),
          MR4 = summary(alpha(d_all_abs %>%
                                rownames_to_column("subject_id") %>% 
                                filter(grepl("s1_", subject_id)) %>%
                                column_to_rownames("subject_id") %>%
                                select(d_all_all_scales %>% 
                                         filter(factor == "MR4") %>% 
                                         pull(question)))),
          MR5 = summary(alpha(d_all_abs %>%
                                rownames_to_column("subject_id") %>% 
                                filter(grepl("s1_", subject_id)) %>%
                                column_to_rownames("subject_id") %>%
                                select(d_all_all_scales %>% 
                                         filter(factor == "MR5") %>% 
                                         pull(question)))),
          MR6 = summary(alpha(d_all_abs %>%
                                rownames_to_column("subject_id") %>% 
                                filter(grepl("s1_", subject_id)) %>%
                                column_to_rownames("subject_id") %>%
                                select(d_all_all_scales %>% 
                                         filter(factor == "MR6") %>% 
                                         pull(question)))))

Reliability analysis   

Reliability analysis   

Reliability analysis   

Reliability analysis   

Reliability analysis   

Reliability analysis

Relationships (analog to Table S20)

d1_abs_subscales <- d1_abs %>%
  rownames_to_column("subject_id") %>%
  gather(question, response, -subject_id) %>%
  mutate(question = gsub("_abs", "", question)) %>%
  left_join(d_all_all_scales %>% distinct(question, factor)) %>% 
  filter(!is.na(factor)) %>%
  mutate(factorlab = recode_factor(factor,
                                   "MR1" = "Synesthesia",
                                   "MR2" = "Aesthetic involvement in nature",
                                   "MR3" = "Altered states of consciousness (a)",
                                   "MR4" = "Imaginative involvement",
                                   "MR5" = "Sensed presence",
                                   "MR6" = "Altered states of consciousness (b)")) %>% 
  group_by(subject_id, factor, factorlab) %>%
  summarise(score = mean(response, na.rm = T)) %>%
  ungroup() %>%
  mutate(subject_id = as.numeric(subject_id)) %>%
  left_join(d1 %>% distinct(subject_id, country)) %>%
  group_by(country, factor, factorlab) %>%
  mutate(score = scale(score)) %>%
  ungroup() %>%
  select(-country, -factor) %>%
  spread(factorlab, score) %>%
  mutate_all(as.numeric) %>%
  left_join(d1_fortab)
Joining, by = "question"
`summarise()` has grouped output by 'subject_id', 'factor'. You can override using the `.groups` argument.
Joining, by = "subject_id"
Joining, by = "subject_id"
# r104_spev <- lm(`Spiritual Events` ~ `Porosity Vignettes`,
#                data = d1_abs_subscales)

r105_spev_MR1 <- lm(`Spiritual Events` ~ Synesthesia,
                    data = d1_abs_subscales)
r105_spev_MR2 <- lm(`Spiritual Events` ~ `Aesthetic involvement in nature`,
                    data = d1_abs_subscales)
r105_spev_MR3 <- lm(`Spiritual Events` ~ `Altered states of consciousness (a)`,
                    data = d1_abs_subscales)
r105_spev_MR4 <- lm(`Spiritual Events` ~ `Imaginative involvement`,
                    data = d1_abs_subscales)
r105_spev_MR5 <- lm(`Spiritual Events` ~ `Sensed presence`,
                    data = d1_abs_subscales)
r105_spev_MR6 <- lm(`Spiritual Events` ~ `Altered states of consciousness (b)`,
                    data = d1_abs_subscales)

r106_spev_MR1 <- lm(`Spiritual Events` ~ `Porosity Vignettes` * Synesthesia,
                    data = d1_abs_subscales)
r106_spev_MR2 <- lm(`Spiritual Events` ~ `Porosity Vignettes` * `Aesthetic involvement in nature`,
                    data = d1_abs_subscales)
r106_spev_MR3 <- lm(`Spiritual Events` ~ `Porosity Vignettes` * `Altered states of consciousness (a)`,
                    data = d1_abs_subscales)
r106_spev_MR4 <- lm(`Spiritual Events` ~ `Porosity Vignettes` * `Imaginative involvement`,
                    data = d1_abs_subscales)
r106_spev_MR5 <- lm(`Spiritual Events` ~ `Porosity Vignettes` * `Sensed presence`,
                    data = d1_abs_subscales)
r106_spev_MR6 <- lm(`Spiritual Events` ~ `Porosity Vignettes` * `Altered states of consciousness (b)`,
                    data = d1_abs_subscales)

# r107_spev <- lm(`Spiritual Events` ~ Country * Site * Religion 
#                + `Porosity Vignettes` + Country:`Porosity Vignettes`,
#                data = d1_abs_subscales)

r108_spev_MR1 <- lm(`Spiritual Events` ~ Country * Site * Religion 
                    + Synesthesia + Country:Synesthesia,
                    data = d1_abs_subscales)
r108_spev_MR2 <- lm(`Spiritual Events` ~ Country * Site * Religion 
                    + `Aesthetic involvement in nature` + Country:`Aesthetic involvement in nature`,
                    data = d1_abs_subscales)
r108_spev_MR3 <- lm(`Spiritual Events` ~ Country * Site * Religion 
                    + `Altered states of consciousness (a)` + Country:`Altered states of consciousness (a)`,
                    data = d1_abs_subscales)
r108_spev_MR4 <- lm(`Spiritual Events` ~ Country * Site * Religion 
                    + `Imaginative involvement` + Country:`Imaginative involvement`,
                    data = d1_abs_subscales)
r108_spev_MR5 <- lm(`Spiritual Events` ~ Country * Site * Religion 
                    + `Sensed presence` + Country:`Sensed presence`,
                    data = d1_abs_subscales)
r108_spev_MR6 <- lm(`Spiritual Events` ~ Country * Site * Religion 
                    + `Altered states of consciousness (b)` + Country:`Altered states of consciousness (b)`,
                    data = d1_abs_subscales)

r109_spev_MR1 <- lm(`Spiritual Events` ~ Country * Site * Religion 
                    + `Porosity Vignettes` + Country:`Porosity Vignettes`
                    + Synesthesia + Country:Synesthesia
                    + `Porosity Vignettes`:Synesthesia 
                    + Country:`Porosity Vignettes`:Synesthesia,
                    data = d1_abs_subscales)
r109_spev_MR2 <- lm(`Spiritual Events` ~ Country * Site * Religion 
                    + `Porosity Vignettes` + Country:`Porosity Vignettes`
                    + `Aesthetic involvement in nature` + Country:`Aesthetic involvement in nature`
                    + `Porosity Vignettes`:`Aesthetic involvement in nature` 
                    + Country:`Porosity Vignettes`:`Aesthetic involvement in nature`,
                    data = d1_abs_subscales)
r109_spev_MR3 <- lm(`Spiritual Events` ~ Country * Site * Religion 
                    + `Porosity Vignettes` + Country:`Porosity Vignettes`
                    + `Altered states of consciousness (a)` + Country:`Altered states of consciousness (a)`
                    + `Porosity Vignettes`:`Altered states of consciousness (a)` 
                    + Country:`Porosity Vignettes`:`Altered states of consciousness (a)`,
                    data = d1_abs_subscales)
r109_spev_MR4 <- lm(`Spiritual Events` ~ Country * Site * Religion 
                    + `Porosity Vignettes` + Country:`Porosity Vignettes`
                    + `Imaginative involvement` + Country:`Imaginative involvement`
                    + `Porosity Vignettes`:`Imaginative involvement` 
                    + Country:`Porosity Vignettes`:`Imaginative involvement`,
                    data = d1_abs_subscales)
r109_spev_MR5 <- lm(`Spiritual Events` ~ Country * Site * Religion 
                    + `Porosity Vignettes` + Country:`Porosity Vignettes`
                    + `Sensed presence` + Country:`Sensed presence`
                    + `Porosity Vignettes`:`Sensed presence` 
                    + Country:`Porosity Vignettes`:`Sensed presence`,
                    data = d1_abs_subscales)
r109_spev_MR6 <- lm(`Spiritual Events` ~ Country * Site * Religion 
                    + `Porosity Vignettes` + Country:`Porosity Vignettes`
                    + `Altered states of consciousness (b)` + Country:`Altered states of consciousness (b)`
                    + `Porosity Vignettes`:`Altered states of consciousness (b)` 
                    + Country:`Porosity Vignettes`:`Altered states of consciousness (b)`,
                    data = d1_abs_subscales)

r110_spev_MR1 <- lm(`Spiritual Events` ~ Country * Site * Religion 
                   + `Porosity Vignettes` + Country:`Porosity Vignettes`
                   + Synesthesia + Country:Synesthesia
                   + `Porosity Vignettes`:Synesthesia
                   + Country:`Porosity Vignettes`:Synesthesia
                   + Gender + Age + Education,
                   data = d1_abs_subscales)
r110_spev_MR2 <- lm(`Spiritual Events` ~ Country * Site * Religion 
                   + `Porosity Vignettes` + Country:`Porosity Vignettes`
                   + `Aesthetic involvement in nature` + Country:`Aesthetic involvement in nature`
                   + `Porosity Vignettes`:`Aesthetic involvement in nature`
                   + Country:`Porosity Vignettes`:`Aesthetic involvement in nature`
                   + Gender + Age + Education,
                   data = d1_abs_subscales)
r110_spev_MR3 <- lm(`Spiritual Events` ~ Country * Site * Religion 
                   + `Porosity Vignettes` + Country:`Porosity Vignettes`
                   + `Altered states of consciousness (a)` + Country:`Altered states of consciousness (a)`
                   + `Porosity Vignettes`:`Altered states of consciousness (a)`
                   + Country:`Porosity Vignettes`:`Altered states of consciousness (a)`
                   + Gender + Age + Education,
                   data = d1_abs_subscales)
r110_spev_MR4 <- lm(`Spiritual Events` ~ Country * Site * Religion 
                   + `Porosity Vignettes` + Country:`Porosity Vignettes`
                   + `Imaginative involvement` + Country:`Imaginative involvement`
                   + `Porosity Vignettes`:`Imaginative involvement`
                   + Country:`Porosity Vignettes`:`Imaginative involvement`
                   + Gender + Age + Education,
                   data = d1_abs_subscales)
r110_spev_MR5 <- lm(`Spiritual Events` ~ Country * Site * Religion 
                   + `Porosity Vignettes` + Country:`Porosity Vignettes`
                   + `Sensed presence` + Country:`Sensed presence`
                   + `Porosity Vignettes`:`Sensed presence`
                   + Country:`Porosity Vignettes`:`Sensed presence`
                   + Gender + Age + Education,
                   data = d1_abs_subscales)
r110_spev_MR6 <- lm(`Spiritual Events` ~ Country * Site * Religion 
                   + `Porosity Vignettes` + Country:`Porosity Vignettes`
                   + `Altered states of consciousness (b)` + Country:`Altered states of consciousness (b)`
                   + `Porosity Vignettes`:`Altered states of consciousness (b)`
                   + Country:`Porosity Vignettes`:`Altered states of consciousness (b)`
                   + Gender + Age + Education,
                   data = d1_abs_subscales)
table_s20_subscales <- stargazer(
  #r104_spev, 
  r105_spev_MR1, r105_spev_MR2, r105_spev_MR3, r105_spev_MR4, r105_spev_MR5, r105_spev_MR6,
  r106_spev_MR1, r106_spev_MR2, r106_spev_MR3, r106_spev_MR4, r106_spev_MR5, r106_spev_MR6,
  #r107_spev, 
  r108_spev_MR1, r108_spev_MR2, r108_spev_MR3, r108_spev_MR4, r108_spev_MR5, r108_spev_MR6,
  r109_spev_MR1, r109_spev_MR2, r109_spev_MR3, r109_spev_MR4, r109_spev_MR5, r109_spev_MR6,
  r110_spev_MR1, r110_spev_MR2, r110_spev_MR3, r110_spev_MR4, r110_spev_MR5, r110_spev_MR6,
  type = "html",
  ci = T,
  report = c("vcsp"),
  digits = 2,
  digits.extra = 1000,
  omit.table.layout = "n",
  # order = c("^`Porosity Vignettes`$",
  #           "^Absorption$",
  #           "^`Porosity Vignettes`:Absorption$"),
  # dep.var.labels.include = F,
  dep.var.caption = "",
  single.row = T)
cat(sub_interact_fun(table_s20_subscales), 
    file = "./stargazer_tables/table_s20_subscales.html")
# htmltools::includeHTML("./stargazer_tables/table_s20_subscales.html")
table_s20_subscales_MR1 <- stargazer(#r104_spev,
                                     r105_spev_MR1, r106_spev_MR1, 
                                     #r107_spev, 
                                     r108_spev_MR1, r109_spev_MR1, r110_spev_MR1, 
                                     type = "html",
                                     ci = T,
                                     report = c("vcsp"),
                                     digits = 2,
                                     digits.extra = 1000,
                                     omit.table.layout = "n",
                                     order = c("^`Porosity Vignettes`$",
                                               "^Synesthesia$",
                                               "^`Porosity Vignettes`:Synesthesia$"),
                                     dep.var.labels.include = F,
                                     dep.var.caption = "",
                                     single.row = T)
cat(sub_interact_fun(table_s20_subscales_MR1), 
    file = "./stargazer_tables/table_s20_subscales_MR1.html")
htmltools::includeHTML("./stargazer_tables/table_s20_subscales_MR1.html")
(1)(2)(3)(4)(5)
Porosity Vignettes0.33 (0.23, 0.44)0.34 (0.19, 0.48)0.31 (0.17, 0.46)
p = 0.00p = 0.00001p = 0.00005
Synesthesia0.14 (0.03, 0.25)0.09 (-0.01, 0.20)0.18 (0.08, 0.28)0.15 (0.04, 0.27)0.16 (0.04, 0.27)
p = 0.02p = 0.09p = 0.0005p = 0.01p = 0.01
Porosity Vignettes:Synesthesia-0.02 (-0.13, 0.09)-0.02 (-0.16, 0.13)-0.02 (-0.17, 0.12)
p = 0.75p = 0.84p = 0.74
Country: Gh.0.14 (-0.02, 0.31)0.02 (-0.20, 0.25)0.01 (-0.22, 0.24)
p = 0.10p = 0.85p = 0.93
Country: Th.-0.03 (-0.24, 0.18)-0.09 (-0.32, 0.14)-0.09 (-0.33, 0.14)
p = 0.78p = 0.45p = 0.45
Country: Ch.-0.41 (-0.61, -0.21)-0.24 (-0.53, 0.06)-0.24 (-0.53, 0.05)
p = 0.0001p = 0.12p = 0.11
Country: Va.0.79 (0.58, 1.01)0.70 (0.47, 0.93)0.67 (0.42, 0.92)
p = 0.00p = 0.00p = 0.0000004
Site: rural0.03 (-0.07, 0.12)0.02 (-0.07, 0.12)0.02 (-0.09, 0.13)
p = 0.62p = 0.64p = 0.74
Religion: CC0.26 (0.16, 0.36)0.25 (0.15, 0.35)0.25 (0.15, 0.35)
p = 0.0000005p = 0.000003p = 0.000003
Gender: male-0.07 (-0.17, 0.03)
p = 0.16
Age-0.08 (-0.19, 0.02)
p = 0.13
Education: at least HS-0.12 (-0.30, 0.06)
p = 0.20
Country: Gh. × Site: rural-0.27 (-0.44, -0.10)-0.28 (-0.45, -0.11)-0.24 (-0.42, -0.06)
p = 0.002p = 0.002p = 0.01
Country: Th. × Site: rural0.02 (-0.20, 0.23)0.002 (-0.21, 0.22)0.01 (-0.20, 0.23)
p = 0.90p = 0.99p = 0.91
Country: Ch. × Site: rural0.43 (0.23, 0.63)0.42 (0.23, 0.62)0.38 (0.17, 0.60)
p = 0.00003p = 0.00004p = 0.001
Country: Va. × Site: rural-0.02 (-0.24, 0.21)0.01 (-0.21, 0.23)0.001 (-0.22, 0.22)
p = 0.89p = 0.91p = 1.00
Country: Gh. × Religion: CC-0.17 (-0.35, 0.01)-0.11 (-0.30, 0.07)-0.09 (-0.31, 0.13)
p = 0.07p = 0.22p = 0.42
Country: Th. × Religion: CC-0.09 (-0.31, 0.13)-0.14 (-0.37, 0.08)-0.15 (-0.38, 0.08)
p = 0.42p = 0.22p = 0.21
Country: Ch. × Religion: CC-0.13 (-0.34, 0.07)-0.13 (-0.33, 0.08)-0.15 (-0.36, 0.05)
p = 0.21p = 0.23p = 0.14
Country: Va. × Religion: CC0.10 (-0.12, 0.32)0.10 (-0.12, 0.33)0.11 (-0.12, 0.33)
p = 0.37p = 0.37p = 0.35
Site: rural × Religion: CC-0.11 (-0.21, -0.01)-0.09 (-0.19, 0.01)-0.12 (-0.22, -0.02)
p = 0.04p = 0.07p = 0.03
Country: Gh. × Porosity Vignettes-0.17 (-0.38, 0.03)-0.16 (-0.37, 0.05)
p = 0.11p = 0.14
Country: Th. × Porosity Vignettes-0.10 (-0.36, 0.16)-0.10 (-0.36, 0.16)
p = 0.44p = 0.47
Country: Ch. × Porosity Vignettes-0.08 (-0.36, 0.20)-0.05 (-0.33, 0.23)
p = 0.58p = 0.74
Country: Va. × Porosity Vignettes0.50 (0.07, 0.93)0.45 (0.02, 0.88)
p = 0.03p = 0.05
Country: Gh. × Synesthesia0.003 (-0.18, 0.19)0.10 (-0.13, 0.33)0.12 (-0.11, 0.35)
p = 0.98p = 0.40p = 0.30
Country: Th. × Synesthesia0.09 (-0.11, 0.28)0.08 (-0.12, 0.29)0.09 (-0.12, 0.30)
p = 0.38p = 0.43p = 0.40
Country: Ch. × Synesthesia-0.01 (-0.21, 0.19)-0.03 (-0.27, 0.22)-0.03 (-0.27, 0.22)
p = 0.96p = 0.83p = 0.84
Country: Va. × Synesthesia-0.09 (-0.31, 0.13)-0.13 (-0.36, 0.09)-0.17 (-0.39, 0.06)
p = 0.44p = 0.25p = 0.16
Country: Gh. × Site: rural × Religion: CC-0.11 (-0.28, 0.07)-0.14 (-0.31, 0.03)-0.16 (-0.33, 0.02)
p = 0.25p = 0.12p = 0.09
Country: Th. × Site: rural × Religion: CC0.30 (0.08, 0.52)0.25 (0.04, 0.47)0.25 (0.04, 0.47)
p = 0.01p = 0.03p = 0.03
Country: Ch. × Site: rural × Religion: CC-0.11 (-0.32, 0.09)-0.11 (-0.31, 0.09)-0.11 (-0.30, 0.09)
p = 0.27p = 0.28p = 0.30
Country: Va. × Site: rural × Religion: CC-0.05 (-0.28, 0.17)0.01 (-0.21, 0.24)0.02 (-0.21, 0.24)
p = 0.64p = 0.90p = 0.90
Country: Gh. × Porosity Vignettes:Synesthesia-0.08 (-0.29, 0.13)-0.09 (-0.30, 0.12)
p = 0.46p = 0.40
Country: Th. × Porosity Vignettes:Synesthesia-0.09 (-0.37, 0.18)-0.11 (-0.39, 0.16)
p = 0.51p = 0.42
Country: Ch. × Porosity Vignettes:Synesthesia0.08 (-0.17, 0.32)0.09 (-0.16, 0.33)
p = 0.54p = 0.50
Country: Va. × Porosity Vignettes:Synesthesia0.12 (-0.31, 0.55)0.13 (-0.30, 0.56)
p = 0.59p = 0.57
Constant-0.01 (-0.12, 0.10)-0.01 (-0.12, 0.09)0.02 (-0.08, 0.12)0.02 (-0.10, 0.14)0.07 (-0.07, 0.21)
p = 0.83p = 0.82p = 0.69p = 0.72p = 0.32
Observations301297301297293
R20.020.130.420.470.49
Adjusted R20.020.120.360.400.42
Residual Std. Error0.98 (df = 299)0.93 (df = 293)0.79 (df = 276)0.77 (df = 262)0.76 (df = 255)
F Statistic6.28** (df = 1; 299)14.75*** (df = 3; 293)8.17*** (df = 24; 276)6.80*** (df = 34; 262)6.60*** (df = 37; 255)
table_s20_subscales_MR2 <- stargazer(#r104_spev,
                                     r105_spev_MR2, r106_spev_MR2, 
                                     #r107_spev, 
                                     r108_spev_MR2, r109_spev_MR2, r110_spev_MR2, 
                                     type = "html",
                                     ci = T,
                                     report = c("vcsp"),
                                     digits = 2,
                                     digits.extra = 1000,
                                     omit.table.layout = "n",
                                     order = c("^`Porosity Vignettes`$",
                                               "^`Aesthetic involvement in nature`$",
                                               "^`Porosity Vignettes`:`Aesthetic involvement in nature`$"),
                                     dep.var.labels.include = F,
                                     dep.var.caption = "",
                                     single.row = T)
cat(sub_interact_fun(table_s20_subscales_MR2), 
    file = "./stargazer_tables/table_s20_subscales_MR2.html")
htmltools::includeHTML("./stargazer_tables/table_s20_subscales_MR2.html")
(1)(2)(3)(4)(5)
Porosity Vignettes0.35 (0.24, 0.46)0.36 (0.22, 0.50)0.34 (0.19, 0.48)
p = 0.00p = 0.000002p = 0.00001
Aesthetic involvement in nature0.08 (-0.04, 0.19)0.04 (-0.07, 0.15)0.12 (0.02, 0.21)0.09 (-0.03, 0.20)0.09 (-0.03, 0.20)
p = 0.19p = 0.48p = 0.02p = 0.14p = 0.14
Porosity Vignettes:Aesthetic involvement in nature-0.10 (-0.21, 0.01)-0.20 (-0.36, -0.05)-0.23 (-0.39, -0.07)
p = 0.07p = 0.02p = 0.005
Country: Gh.0.14 (-0.04, 0.31)0.07 (-0.16, 0.29)0.05 (-0.19, 0.28)
p = 0.13p = 0.57p = 0.70
Country: Th.-0.03 (-0.25, 0.18)-0.12 (-0.35, 0.10)-0.12 (-0.35, 0.12)
p = 0.76p = 0.30p = 0.33
Country: Ch.-0.40 (-0.60, -0.20)-0.18 (-0.43, 0.08)-0.17 (-0.43, 0.08)
p = 0.0002p = 0.18p = 0.19
Country: Va.0.80 (0.58, 1.01)0.67 (0.45, 0.89)0.61 (0.36, 0.86)
p = 0.00p = 0.00p = 0.000003
Site: rural0.03 (-0.07, 0.13)0.03 (-0.07, 0.12)0.01 (-0.10, 0.13)
p = 0.52p = 0.57p = 0.83
Religion: CC0.24 (0.14, 0.34)0.23 (0.13, 0.33)0.23 (0.14, 0.33)
p = 0.000003p = 0.00001p = 0.000005
Gender: male-0.05 (-0.14, 0.04)
p = 0.30
Age-0.09 (-0.19, 0.02)
p = 0.12
Education: at least HS-0.14 (-0.33, 0.04)
p = 0.12
Country: Gh. × Site: rural-0.27 (-0.44, -0.10)-0.29 (-0.45, -0.12)-0.23 (-0.41, -0.06)
p = 0.003p = 0.002p = 0.01
Country: Th. × Site: rural0.02 (-0.21, 0.24)0.02 (-0.20, 0.24)0.04 (-0.18, 0.25)
p = 0.90p = 0.86p = 0.75
Country: Ch. × Site: rural0.44 (0.24, 0.64)0.43 (0.23, 0.63)0.38 (0.16, 0.59)
p = 0.00003p = 0.00003p = 0.001
Country: Va. × Site: rural-0.0001 (-0.22, 0.22)0.01 (-0.20, 0.22)-0.02 (-0.23, 0.19)
p = 1.00p = 0.95p = 0.86
Country: Gh. × Religion: CC-0.18 (-0.36, -0.01)-0.12 (-0.29, 0.05)-0.09 (-0.29, 0.12)
p = 0.04p = 0.17p = 0.40
Country: Th. × Religion: CC-0.08 (-0.30, 0.14)-0.12 (-0.34, 0.10)-0.13 (-0.36, 0.10)
p = 0.50p = 0.28p = 0.27
Country: Ch. × Religion: CC-0.16 (-0.36, 0.04)-0.12 (-0.31, 0.07)-0.15 (-0.35, 0.04)
p = 0.12p = 0.22p = 0.13
Country: Va. × Religion: CC0.11 (-0.10, 0.33)0.08 (-0.13, 0.29)0.09 (-0.12, 0.30)
p = 0.31p = 0.45p = 0.41
Site: rural × Religion: CC-0.12 (-0.21, -0.02)-0.10 (-0.19, 0.001)-0.12 (-0.22, -0.02)
p = 0.03p = 0.06p = 0.02
Country: Gh. × Porosity Vignettes-0.24 (-0.45, -0.03)-0.23 (-0.44, -0.02)
p = 0.03p = 0.04
Country: Th. × Porosity Vignettes-0.08 (-0.34, 0.18)-0.08 (-0.34, 0.18)
p = 0.54p = 0.56
Country: Ch. × Porosity Vignettes-0.06 (-0.32, 0.19)-0.03 (-0.29, 0.23)
p = 0.64p = 0.83
Country: Va. × Porosity Vignettes0.56 (0.14, 0.98)0.50 (0.08, 0.92)
p = 0.01p = 0.03
Country: Gh. × Aesthetic involvement in nature0.04 (-0.13, 0.22)0.17 (-0.05, 0.38)0.18 (-0.04, 0.39)
p = 0.63p = 0.13p = 0.11
Country: Th. × Aesthetic involvement in nature0.12 (-0.08, 0.32)0.13 (-0.08, 0.34)0.13 (-0.08, 0.34)
p = 0.26p = 0.23p = 0.24
Country: Ch. × Aesthetic involvement in nature0.04 (-0.15, 0.23)-0.11 (-0.41, 0.18)-0.15 (-0.45, 0.14)
p = 0.70p = 0.47p = 0.32
Country: Va. × Aesthetic involvement in nature-0.10 (-0.30, 0.11)-0.01 (-0.22, 0.21)0.02 (-0.19, 0.24)
p = 0.37p = 0.95p = 0.84
Country: Gh. × Site: rural × Religion: CC-0.07 (-0.24, 0.10)-0.11 (-0.28, 0.05)-0.13 (-0.30, 0.05)
p = 0.42p = 0.19p = 0.16
Country: Th. × Site: rural × Religion: CC0.26 (0.05, 0.48)0.20 (-0.01, 0.41)0.19 (-0.03, 0.40)
p = 0.02p = 0.07p = 0.10
Country: Ch. × Site: rural × Religion: CC-0.13 (-0.33, 0.07)-0.11 (-0.31, 0.08)-0.11 (-0.30, 0.08)
p = 0.22p = 0.25p = 0.26
Country: Va. × Site: rural × Religion: CC-0.08 (-0.30, 0.14)0.01 (-0.21, 0.22)0.02 (-0.20, 0.23)
p = 0.46p = 0.95p = 0.87
Country: Gh. × Porosity Vignettes:Aesthetic involvement in nature0.10 (-0.11, 0.31)0.10 (-0.11, 0.30)
p = 0.36p = 0.36
Country: Th. × Porosity Vignettes:Aesthetic involvement in nature0.06 (-0.21, 0.34)0.08 (-0.20, 0.36)
p = 0.65p = 0.57
Country: Ch. × Porosity Vignettes:Aesthetic involvement in nature0.05 (-0.24, 0.34)0.04 (-0.25, 0.33)
p = 0.74p = 0.77
Country: Va. × Porosity Vignettes:Aesthetic involvement in nature-0.23 (-0.71, 0.24)-0.28 (-0.76, 0.19)
p = 0.35p = 0.25
Constant-0.01 (-0.12, 0.10)-0.01 (-0.11, 0.10)0.02 (-0.08, 0.12)0.04 (-0.07, 0.16)0.10 (-0.03, 0.24)
p = 0.83p = 0.92p = 0.66p = 0.48p = 0.15
Observations301297301297293
R20.010.130.400.480.50
Adjusted R20.0020.130.350.410.42
Residual Std. Error0.98 (df = 299)0.93 (df = 293)0.79 (df = 276)0.76 (df = 262)0.75 (df = 255)
F Statistic1.73 (df = 1; 299)15.22*** (df = 3; 293)7.76*** (df = 24; 276)7.01*** (df = 34; 262)6.77*** (df = 37; 255)
table_s20_subscales_MR3 <- stargazer(#r104_spev,
                                     r105_spev_MR3, r106_spev_MR3, 
                                     #r107_spev, 
                                     r108_spev_MR3, r109_spev_MR3, r110_spev_MR3, 
                                     type = "html",
                                     ci = T,
                                     report = c("vcsp"),
                                     digits = 2,
                                     digits.extra = 1000,
                                     omit.table.layout = "n",
                                     order = c("^`Porosity Vignettes`$",
                                               "^`Altered states of consciousness \\(a\\)`$",
                                               "^`Porosity Vignettes`:`Altered states of consciousness \\(a\\)`$"),
                                     dep.var.labels.include = F,
                                     dep.var.caption = "",
                                     single.row = T)
cat(sub_interact_fun(table_s20_subscales_MR3), 
    file = "./stargazer_tables/table_s20_subscales_MR3.html")
htmltools::includeHTML("./stargazer_tables/table_s20_subscales_MR3.html")
(1)(2)(3)(4)(5)
Porosity Vignettes0.33 (0.23, 0.44)0.36 (0.22, 0.50)0.33 (0.19, 0.47)
p = 0.00p = 0.000002p = 0.00002
Altered states of consciousness (a)0.14 (0.03, 0.26)0.10 (-0.003, 0.21)0.14 (0.04, 0.23)0.11 (-0.01, 0.22)0.13 (0.01, 0.24)
p = 0.02p = 0.06p = 0.01p = 0.08p = 0.04
Porosity Vignettes:Altered states of consciousness (a)-0.04 (-0.15, 0.07)0.03 (-0.12, 0.17)0.01 (-0.14, 0.15)
p = 0.49p = 0.72p = 0.94
Country: Gh.0.14 (-0.03, 0.31)0.09 (-0.14, 0.32)0.10 (-0.14, 0.33)
p = 0.11p = 0.45p = 0.43
Country: Th.-0.04 (-0.25, 0.18)-0.15 (-0.37, 0.07)-0.14 (-0.37, 0.09)
p = 0.73p = 0.20p = 0.23
Country: Ch.-0.40 (-0.60, -0.21)-0.20 (-0.46, 0.06)-0.22 (-0.47, 0.04)
p = 0.0001p = 0.14p = 0.11
Country: Va.0.80 (0.59, 1.01)0.69 (0.47, 0.91)0.62 (0.37, 0.86)
p = 0.00p = 0.00p = 0.000002
Site: rural0.06 (-0.04, 0.15)0.05 (-0.05, 0.15)0.03 (-0.09, 0.14)
p = 0.28p = 0.31p = 0.67
Religion: CC0.23 (0.13, 0.32)0.22 (0.12, 0.32)0.23 (0.13, 0.32)
p = 0.00001p = 0.00002p = 0.00002
Gender: male-0.06 (-0.16, 0.03)
p = 0.19
Age-0.07 (-0.18, 0.03)
p = 0.18
Education: at least HS-0.17 (-0.35, 0.01)
p = 0.07
Country: Gh. × Site: rural-0.21 (-0.39, -0.03)-0.24 (-0.42, -0.07)-0.19 (-0.38, -0.01)
p = 0.03p = 0.01p = 0.05
Country: Th. × Site: rural-0.02 (-0.23, 0.20)-0.01 (-0.23, 0.21)0.01 (-0.21, 0.22)
p = 0.89p = 0.91p = 0.97
Country: Ch. × Site: rural0.44 (0.24, 0.65)0.45 (0.25, 0.64)0.38 (0.17, 0.59)
p = 0.00003p = 0.00002p = 0.0005
Country: Va. × Site: rural-0.005 (-0.22, 0.21)0.02 (-0.19, 0.23)-0.01 (-0.22, 0.20)
p = 0.97p = 0.88p = 0.92
Country: Gh. × Religion: CC-0.22 (-0.39, -0.05)-0.18 (-0.35, -0.01)-0.12 (-0.32, 0.08)
p = 0.02p = 0.05p = 0.24
Country: Th. × Religion: CC-0.06 (-0.28, 0.15)-0.10 (-0.32, 0.13)-0.13 (-0.36, 0.10)
p = 0.56p = 0.40p = 0.28
Country: Ch. × Religion: CC-0.13 (-0.32, 0.07)-0.10 (-0.29, 0.09)-0.13 (-0.32, 0.06)
p = 0.21p = 0.32p = 0.19
Country: Va. × Religion: CC0.11 (-0.11, 0.33)0.10 (-0.11, 0.31)0.10 (-0.11, 0.32)
p = 0.33p = 0.36p = 0.34
Site: rural × Religion: CC-0.12 (-0.22, -0.02)-0.10 (-0.19, -0.001)-0.12 (-0.22, -0.03)
p = 0.02p = 0.05p = 0.02
Country: Gh. × Porosity Vignettes-0.29 (-0.50, -0.08)-0.29 (-0.50, -0.08)
p = 0.01p = 0.01
Country: Th. × Porosity Vignettes-0.07 (-0.32, 0.18)-0.06 (-0.31, 0.20)
p = 0.58p = 0.67
Country: Ch. × Porosity Vignettes-0.05 (-0.31, 0.21)0.001 (-0.26, 0.26)
p = 0.72p = 1.00
Country: Va. × Porosity Vignettes0.58 (0.15, 1.00)0.49 (0.06, 0.92)
p = 0.01p = 0.03
Country: Gh. × Altered states of consciousness (a)0.13 (-0.05, 0.31)0.18 (-0.02, 0.39)0.21 (0.01, 0.42)
p = 0.15p = 0.09p = 0.05
Country: Th. × Altered states of consciousness (a)0.08 (-0.11, 0.28)0.12 (-0.10, 0.33)0.10 (-0.11, 0.32)
p = 0.40p = 0.29p = 0.36
Country: Ch. × Altered states of consciousness (a)0.08 (-0.11, 0.28)-0.01 (-0.30, 0.28)-0.02 (-0.31, 0.27)
p = 0.39p = 0.95p = 0.91
Country: Va. × Altered states of consciousness (a)-0.26 (-0.47, -0.06)-0.22 (-0.43, -0.01)-0.23 (-0.44, -0.02)
p = 0.02p = 0.05p = 0.04
Country: Gh. × Site: rural × Religion: CC-0.07 (-0.24, 0.09)-0.12 (-0.28, 0.05)-0.13 (-0.30, 0.04)
p = 0.40p = 0.16p = 0.13
Country: Th. × Site: rural × Religion: CC0.29 (0.07, 0.50)0.23 (0.03, 0.44)0.22 (0.01, 0.43)
p = 0.01p = 0.03p = 0.05
Country: Ch. × Site: rural × Religion: CC-0.14 (-0.33, 0.06)-0.13 (-0.33, 0.06)-0.13 (-0.32, 0.06)
p = 0.17p = 0.17p = 0.19
Country: Va. × Site: rural × Religion: CC-0.09 (-0.31, 0.12)0.003 (-0.21, 0.22)0.01 (-0.20, 0.23)
p = 0.42p = 0.98p = 0.91
Country: Gh. × Porosity Vignettes:Altered states of consciousness (a)-0.13 (-0.33, 0.07)-0.17 (-0.37, 0.03)
p = 0.22p = 0.10
Country: Th. × Porosity Vignettes:Altered states of consciousness (a)-0.003 (-0.31, 0.30)-0.01 (-0.32, 0.29)
p = 0.99p = 0.94
Country: Ch. × Porosity Vignettes:Altered states of consciousness (a)-0.14 (-0.42, 0.15)-0.10 (-0.38, 0.18)
p = 0.36p = 0.48
Country: Va. × Porosity Vignettes:Altered states of consciousness (a)0.37 (-0.02, 0.76)0.36 (-0.03, 0.74)
p = 0.07p = 0.08
Constant-0.01 (-0.12, 0.10)-0.01 (-0.12, 0.10)0.02 (-0.07, 0.12)0.06 (-0.05, 0.17)0.13 (-0.003, 0.27)
p = 0.84p = 0.86p = 0.65p = 0.31p = 0.06
Observations301297301297293
R20.020.140.420.480.50
Adjusted R20.020.130.370.420.43
Residual Std. Error0.98 (df = 299)0.93 (df = 293)0.78 (df = 276)0.76 (df = 262)0.75 (df = 255)
F Statistic6.43** (df = 1; 299)15.32*** (df = 3; 293)8.35*** (df = 24; 276)7.19*** (df = 34; 262)7.00*** (df = 37; 255)
table_s20_subscales_MR4 <- stargazer(#r104_spev,
                                     r105_spev_MR4, r106_spev_MR4, 
                                     #r107_spev, 
                                     r108_spev_MR4, r109_spev_MR4, r110_spev_MR4, 
                                     type = "html",
                                     ci = T,
                                     report = c("vcsp"),
                                     digits = 2,
                                     digits.extra = 1000,
                                     omit.table.layout = "n",
                                     order = c("^`Porosity Vignettes`$",
                                               "^`Imaginative involvement`$",
                                               "^`Porosity Vignettes`:`Imaginative involvement`$"),
                                     dep.var.labels.include = F,
                                     dep.var.caption = "",
                                     single.row = T)
cat(sub_interact_fun(table_s20_subscales_MR4), 
    file = "./stargazer_tables/table_s20_subscales_MR4.html")
htmltools::includeHTML("./stargazer_tables/table_s20_subscales_MR4.html")
(1)(2)(3)(4)(5)
Porosity Vignettes0.34 (0.23, 0.44)0.32 (0.17, 0.47)0.30 (0.15, 0.45)
p = 0.00p = 0.0001p = 0.0002
Imaginative involvement0.09 (-0.02, 0.20)0.05 (-0.06, 0.16)0.15 (0.06, 0.25)0.15 (0.03, 0.28)0.14 (0.02, 0.27)
p = 0.12p = 0.35p = 0.002p = 0.02p = 0.03
Porosity Vignettes:Imaginative involvement0.02 (-0.09, 0.13)0.02 (-0.12, 0.15)-0.003 (-0.14, 0.14)
p = 0.75p = 0.83p = 0.98
Country: Gh.0.14 (-0.03, 0.31)0.08 (-0.15, 0.31)0.06 (-0.18, 0.30)
p = 0.10p = 0.52p = 0.65
Country: Th.-0.06 (-0.27, 0.15)-0.14 (-0.37, 0.09)-0.15 (-0.39, 0.09)
p = 0.59p = 0.23p = 0.22
Country: Ch.-0.40 (-0.59, -0.20)-0.27 (-0.58, 0.05)-0.25 (-0.56, 0.06)
p = 0.0001p = 0.10p = 0.12
Country: Va.0.81 (0.59, 1.02)0.72 (0.49, 0.95)0.70 (0.44, 0.95)
p = 0.00p = 0.00p = 0.0000002
Site: rural0.03 (-0.07, 0.13)0.03 (-0.07, 0.12)0.02 (-0.09, 0.14)
p = 0.52p = 0.60p = 0.69
Religion: CC0.23 (0.14, 0.33)0.22 (0.12, 0.32)0.22 (0.13, 0.32)
p = 0.000005p = 0.00002p = 0.00002
Gender: male-0.07 (-0.17, 0.02)
p = 0.13
Age-0.08 (-0.19, 0.03)
p = 0.18
Education: at least HS-0.10 (-0.28, 0.08)
p = 0.27
Country: Gh. × Site: rural-0.24 (-0.42, -0.07)-0.25 (-0.42, -0.08)-0.23 (-0.41, -0.05)
p = 0.01p = 0.005p = 0.02
Country: Th. × Site: rural-0.05 (-0.26, 0.16)-0.05 (-0.26, 0.16)-0.03 (-0.25, 0.18)
p = 0.65p = 0.66p = 0.77
Country: Ch. × Site: rural0.45 (0.25, 0.65)0.45 (0.25, 0.64)0.41 (0.20, 0.63)
p = 0.00002p = 0.00002p = 0.0003
Country: Va. × Site: rural0.03 (-0.19, 0.24)0.02 (-0.20, 0.24)0.02 (-0.20, 0.24)
p = 0.81p = 0.83p = 0.87
Country: Gh. × Religion: CC-0.23 (-0.40, -0.06)-0.18 (-0.35, -0.004)-0.16 (-0.37, 0.05)
p = 0.01p = 0.05p = 0.14
Country: Th. × Religion: CC-0.12 (-0.33, 0.09)-0.15 (-0.37, 0.06)-0.15 (-0.38, 0.08)
p = 0.28p = 0.17p = 0.21
Country: Ch. × Religion: CC-0.13 (-0.33, 0.06)-0.12 (-0.32, 0.08)-0.15 (-0.35, 0.05)
p = 0.19p = 0.24p = 0.15
Country: Va. × Religion: CC0.17 (-0.05, 0.38)0.14 (-0.07, 0.36)0.15 (-0.07, 0.37)
p = 0.14p = 0.20p = 0.19
Site: rural × Religion: CC-0.12 (-0.22, -0.02)-0.10 (-0.20, -0.01)-0.12 (-0.22, -0.02)
p = 0.02p = 0.05p = 0.02
Country: Gh. × Porosity Vignettes-0.22 (-0.44, 0.0003)-0.18 (-0.41, 0.04)
p = 0.06p = 0.11
Country: Th. × Porosity Vignettes-0.03 (-0.29, 0.23)-0.03 (-0.30, 0.23)
p = 0.81p = 0.80
Country: Ch. × Porosity Vignettes-0.10 (-0.40, 0.21)-0.06 (-0.37, 0.25)
p = 0.54p = 0.73
Country: Va. × Porosity Vignettes0.45 (0.01, 0.89)0.39 (-0.06, 0.83)
p = 0.05p = 0.10
Country: Gh. × Imaginative involvement0.07 (-0.12, 0.25)0.09 (-0.14, 0.32)0.08 (-0.16, 0.31)
p = 0.49p = 0.46p = 0.53
Country: Th. × Imaginative involvement-0.10 (-0.29, 0.09)-0.09 (-0.31, 0.13)-0.08 (-0.30, 0.14)
p = 0.30p = 0.43p = 0.47
Country: Ch. × Imaginative involvement0.04 (-0.16, 0.23)0.07 (-0.28, 0.41)0.04 (-0.31, 0.38)
p = 0.73p = 0.72p = 0.83
Country: Va. × Imaginative involvement0.10 (-0.11, 0.30)0.03 (-0.20, 0.26)0.05 (-0.18, 0.28)
p = 0.36p = 0.78p = 0.65
Country: Gh. × Site: rural × Religion: CC-0.13 (-0.31, 0.05)-0.18 (-0.36, 0.003)-0.17 (-0.35, 0.01)
p = 0.18p = 0.06p = 0.07
Country: Th. × Site: rural × Religion: CC0.29 (0.07, 0.50)0.24 (0.03, 0.46)0.24 (0.02, 0.46)
p = 0.01p = 0.03p = 0.04
Country: Ch. × Site: rural × Religion: CC-0.12 (-0.32, 0.08)-0.11 (-0.30, 0.09)-0.11 (-0.31, 0.08)
p = 0.23p = 0.29p = 0.27
Country: Va. × Site: rural × Religion: CC-0.05 (-0.27, 0.16)0.02 (-0.20, 0.24)0.02 (-0.20, 0.24)
p = 0.63p = 0.84p = 0.84
Country: Gh. × Porosity Vignettes:Imaginative involvement-0.04 (-0.25, 0.18)-0.08 (-0.29, 0.14)
p = 0.75p = 0.49
Country: Th. × Porosity Vignettes:Imaginative involvement0.01 (-0.23, 0.25)-0.02 (-0.27, 0.22)
p = 0.96p = 0.85
Country: Ch. × Porosity Vignettes:Imaginative involvement0.09 (-0.23, 0.40)0.08 (-0.23, 0.40)
p = 0.60p = 0.62
Country: Va. × Porosity Vignettes:Imaginative involvement-0.04 (-0.41, 0.32)-0.001 (-0.36, 0.36)
p = 0.82p = 1.00
Constant-0.01 (-0.12, 0.10)-0.02 (-0.12, 0.09)0.02 (-0.08, 0.11)0.02 (-0.10, 0.15)0.07 (-0.07, 0.21)
p = 0.82p = 0.76p = 0.75p = 0.70p = 0.36
Observations301297301297293
R20.010.130.410.460.48
Adjusted R20.0050.120.360.390.40
Residual Std. Error0.98 (df = 299)0.93 (df = 293)0.79 (df = 276)0.77 (df = 262)0.77 (df = 255)
F Statistic2.49 (df = 1; 299)13.98*** (df = 3; 293)8.00*** (df = 24; 276)6.64*** (df = 34; 262)6.30*** (df = 37; 255)
table_s20_subscales_MR5 <- stargazer(#r104_spev,
                                     r105_spev_MR5, r106_spev_MR5, 
                                     #r107_spev, 
                                     r108_spev_MR5, r109_spev_MR5, r110_spev_MR5, 
                                     type = "html",
                                     ci = T,
                                     report = c("vcsp"),
                                     digits = 2,
                                     digits.extra = 1000,
                                     omit.table.layout = "n",
                                     order = c("^`Porosity Vignettes`$",
                                               "^`Sensed presence`$",
                                               "^`Porosity Vignettes`:`Sensed presence`$"),
                                     dep.var.labels.include = F,
                                     dep.var.caption = "",
                                     single.row = T)
cat(sub_interact_fun(table_s20_subscales_MR5), 
    file = "./stargazer_tables/table_s20_subscales_MR5.html")
htmltools::includeHTML("./stargazer_tables/table_s20_subscales_MR5.html")
(1)(2)(3)(4)(5)
Porosity Vignettes0.32 (0.21, 0.42)0.33 (0.19, 0.47)0.31 (0.17, 0.45)
p = 0.00p = 0.00001p = 0.00002
Sensed presence0.26 (0.15, 0.37)0.22 (0.11, 0.32)0.27 (0.17, 0.36)0.23 (0.13, 0.34)0.23 (0.12, 0.34)
p = 0.00001p = 0.0001p = 0.00000005p = 0.00003p = 0.00005
Porosity Vignettes:Sensed presence-0.0003 (-0.11, 0.11)-0.11 (-0.25, 0.03)-0.14 (-0.28, 0.002)
p = 1.00p = 0.12p = 0.06
Country: Gh.0.16 (-0.01, 0.32)0.04 (-0.18, 0.26)0.02 (-0.20, 0.25)
p = 0.07p = 0.71p = 0.85
Country: Th.-0.07 (-0.27, 0.14)-0.15 (-0.37, 0.06)-0.14 (-0.36, 0.08)
p = 0.52p = 0.17p = 0.23
Country: Ch.-0.39 (-0.58, -0.20)-0.18 (-0.44, 0.08)-0.18 (-0.44, 0.08)
p = 0.0001p = 0.19p = 0.18
Country: Va.0.79 (0.58, 0.99)0.70 (0.48, 0.92)0.65 (0.41, 0.89)
p = 0.00p = 0.00p = 0.0000002
Site: rural-0.01 (-0.10, 0.09)-0.003 (-0.10, 0.09)-0.01 (-0.12, 0.10)
p = 0.92p = 0.96p = 0.86
Religion: CC0.23 (0.14, 0.33)0.21 (0.12, 0.31)0.21 (0.12, 0.31)
p = 0.000004p = 0.00002p = 0.00002
Gender: male-0.02 (-0.11, 0.07)
p = 0.62
Age-0.10 (-0.20, 0.01)
p = 0.09
Education: at least HS-0.13 (-0.30, 0.04)
p = 0.15
Country: Gh. × Site: rural-0.19 (-0.35, -0.02)-0.21 (-0.38, -0.04)-0.16 (-0.34, 0.02)
p = 0.03p = 0.02p = 0.09
Country: Th. × Site: rural-0.02 (-0.23, 0.18)-0.02 (-0.22, 0.19)-0.01 (-0.22, 0.19)
p = 0.84p = 0.86p = 0.90
Country: Ch. × Site: rural0.41 (0.22, 0.61)0.45 (0.26, 0.64)0.42 (0.21, 0.63)
p = 0.0001p = 0.00001p = 0.0002
Country: Va. × Site: rural-0.07 (-0.28, 0.15)-0.07 (-0.29, 0.14)-0.11 (-0.32, 0.11)
p = 0.55p = 0.50p = 0.33
Country: Gh. × Religion: CC-0.14 (-0.31, 0.02)-0.08 (-0.25, 0.08)-0.06 (-0.26, 0.14)
p = 0.10p = 0.32p = 0.55
Country: Th. × Religion: CC-0.14 (-0.35, 0.06)-0.18 (-0.38, 0.03)-0.18 (-0.40, 0.04)
p = 0.17p = 0.10p = 0.11
Country: Ch. × Religion: CC-0.12 (-0.32, 0.08)-0.11 (-0.31, 0.09)-0.15 (-0.35, 0.05)
p = 0.25p = 0.29p = 0.16
Country: Va. × Religion: CC0.09 (-0.12, 0.30)0.06 (-0.15, 0.27)0.08 (-0.13, 0.29)
p = 0.39p = 0.57p = 0.47
Site: rural × Religion: CC-0.15 (-0.25, -0.06)-0.14 (-0.23, -0.04)-0.16 (-0.25, -0.06)
p = 0.002p = 0.01p = 0.002
Country: Gh. × Porosity Vignettes-0.21 (-0.42, -0.01)-0.21 (-0.41, -0.005)
p = 0.05p = 0.05
Country: Th. × Porosity Vignettes-0.08 (-0.33, 0.17)-0.06 (-0.31, 0.19)
p = 0.55p = 0.66
Country: Ch. × Porosity Vignettes-0.003 (-0.26, 0.25)0.02 (-0.24, 0.28)
p = 0.99p = 0.90
Country: Va. × Porosity Vignettes0.40 (-0.02, 0.81)0.35 (-0.07, 0.76)
p = 0.07p = 0.11
Country: Gh. × Sensed presence0.04 (-0.14, 0.22)0.11 (-0.10, 0.31)0.14 (-0.06, 0.34)
p = 0.64p = 0.32p = 0.18
Country: Th. × Sensed presence0.12 (-0.07, 0.31)0.16 (-0.04, 0.35)0.15 (-0.05, 0.35)
p = 0.21p = 0.13p = 0.15
Country: Ch. × Sensed presence-0.17 (-0.37, 0.03)-0.24 (-0.48, 0.004)-0.26 (-0.50, -0.02)
p = 0.09p = 0.06p = 0.04
Country: Va. × Sensed presence0.12 (-0.08, 0.32)0.09 (-0.12, 0.30)0.08 (-0.13, 0.29)
p = 0.25p = 0.40p = 0.44
Country: Gh. × Site: rural × Religion: CC-0.13 (-0.31, 0.04)-0.18 (-0.35, -0.01)-0.21 (-0.39, -0.04)
p = 0.14p = 0.05p = 0.02
Country: Th. × Site: rural × Religion: CC0.19 (-0.02, 0.41)0.16 (-0.05, 0.36)0.15 (-0.06, 0.36)
p = 0.08p = 0.15p = 0.17
Country: Ch. × Site: rural × Religion: CC-0.10 (-0.29, 0.09)-0.09 (-0.28, 0.10)-0.09 (-0.27, 0.10)
p = 0.30p = 0.36p = 0.37
Country: Va. × Site: rural × Religion: CC-0.02 (-0.22, 0.19)0.05 (-0.15, 0.26)0.08 (-0.13, 0.29)
p = 0.89p = 0.61p = 0.46
Country: Gh. × Porosity Vignettes:Sensed presence0.06 (-0.13, 0.25)0.06 (-0.13, 0.25)
p = 0.55p = 0.53
Country: Th. × Porosity Vignettes:Sensed presence-0.02 (-0.27, 0.22)-0.04 (-0.29, 0.21)
p = 0.87p = 0.75
Country: Ch. × Porosity Vignettes:Sensed presence0.07 (-0.17, 0.31)0.11 (-0.13, 0.35)
p = 0.56p = 0.37
Country: Va. × Porosity Vignettes:Sensed presence-0.16 (-0.57, 0.25)-0.20 (-0.60, 0.21)
p = 0.46p = 0.35
Constant-0.01 (-0.12, 0.09)-0.01 (-0.12, 0.09)0.004 (-0.09, 0.10)0.05 (-0.06, 0.16)0.11 (-0.02, 0.24)
p = 0.80p = 0.79p = 0.93p = 0.42p = 0.12
Observations301297301297293
R20.070.170.460.510.53
Adjusted R20.060.160.410.450.46
Residual Std. Error0.95 (df = 299)0.91 (df = 293)0.76 (df = 276)0.74 (df = 262)0.73 (df = 255)
F Statistic21.55*** (df = 1; 299)19.97*** (df = 3; 293)9.69*** (df = 24; 276)8.00*** (df = 34; 262)7.69*** (df = 37; 255)
table_s20_subscales_MR6 <- stargazer(#r104_spev,
                                     r105_spev_MR6, r106_spev_MR6, 
                                     #r107_spev, 
                                     r108_spev_MR6, r109_spev_MR6, r110_spev_MR6, 
                                     type = "html",
                                     ci = T,
                                     report = c("vcsp"),
                                     digits = 2,
                                     digits.extra = 1000,
                                     omit.table.layout = "n",
                                     order = c("^`Porosity Vignettes`$",
                                               "^`Altered states of consciousness \\(b\\)`$",
                                               "^`Porosity Vignettes`:`Altered states of consciousness \\(b\\)`$"),
                                     dep.var.labels.include = F,
                                     dep.var.caption = "",
                                     single.row = T)
cat(sub_interact_fun(table_s20_subscales_MR6), 
    file = "./stargazer_tables/table_s20_subscales_MR6.html")
htmltools::includeHTML("./stargazer_tables/table_s20_subscales_MR6.html")
(1)(2)(3)(4)(5)
Porosity Vignettes0.33 (0.22, 0.43)0.32 (0.18, 0.46)0.29 (0.15, 0.44)
p = 0.00p = 0.00002p = 0.0001
Altered states of consciousness (b)0.18 (0.07, 0.29)0.12 (0.02, 0.23)0.19 (0.10, 0.29)0.16 (0.05, 0.28)0.17 (0.06, 0.29)
p = 0.002p = 0.03p = 0.0001p = 0.01p = 0.004
Porosity Vignettes:Altered states of consciousness (b)-0.02 (-0.13, 0.09)0.03 (-0.14, 0.20)0.03 (-0.15, 0.20)
p = 0.71p = 0.75p = 0.77
Country: Gh.0.15 (-0.02, 0.31)0.07 (-0.16, 0.31)0.08 (-0.16, 0.32)
p = 0.10p = 0.54p = 0.53
Country: Th.-0.07 (-0.28, 0.15)-0.13 (-0.36, 0.10)-0.14 (-0.38, 0.10)
p = 0.55p = 0.28p = 0.25
Country: Ch.-0.42 (-0.62, -0.23)-0.26 (-0.54, 0.02)-0.26 (-0.54, 0.02)
p = 0.00004p = 0.07p = 0.07
Country: Va.0.83 (0.62, 1.05)0.71 (0.48, 0.94)0.70 (0.44, 0.95)
p = 0.00p = 0.00p = 0.0000002
Site: rural0.03 (-0.07, 0.13)0.04 (-0.06, 0.14)0.05 (-0.06, 0.17)
p = 0.54p = 0.41p = 0.38
Religion: CC0.25 (0.15, 0.34)0.24 (0.14, 0.34)0.24 (0.14, 0.34)
p = 0.000002p = 0.000003p = 0.000004
Gender: male-0.08 (-0.18, 0.01)
p = 0.08
Age-0.09 (-0.20, 0.01)
p = 0.10
Education: at least HS-0.08 (-0.26, 0.10)
p = 0.38
Country: Gh. × Site: rural-0.27 (-0.44, -0.10)-0.30 (-0.47, -0.13)-0.27 (-0.45, -0.09)
p = 0.002p = 0.001p = 0.004
Country: Th. × Site: rural-0.06 (-0.28, 0.16)-0.05 (-0.26, 0.17)-0.04 (-0.25, 0.18)
p = 0.60p = 0.69p = 0.75
Country: Ch. × Site: rural0.44 (0.24, 0.63)0.43 (0.24, 0.62)0.41 (0.20, 0.62)
p = 0.00002p = 0.00003p = 0.0002
Country: Va. × Site: rural0.04 (-0.18, 0.25)0.07 (-0.16, 0.29)0.06 (-0.16, 0.28)
p = 0.73p = 0.56p = 0.61
Country: Gh. × Religion: CC-0.17 (-0.34, 0.01)-0.12 (-0.29, 0.05)-0.13 (-0.33, 0.08)
p = 0.07p = 0.17p = 0.23
Country: Th. × Religion: CC-0.13 (-0.34, 0.08)-0.18 (-0.39, 0.04)-0.16 (-0.38, 0.07)
p = 0.24p = 0.11p = 0.18
Country: Ch. × Religion: CC-0.08 (-0.28, 0.12)-0.08 (-0.28, 0.12)-0.11 (-0.31, 0.09)
p = 0.45p = 0.45p = 0.29
Country: Va. × Religion: CC0.10 (-0.11, 0.32)0.13 (-0.09, 0.35)0.14 (-0.08, 0.36)
p = 0.36p = 0.27p = 0.22
Site: rural × Religion: CC-0.11 (-0.21, -0.01)-0.10 (-0.20, -0.0001)-0.12 (-0.22, -0.02)
p = 0.03p = 0.06p = 0.02
Country: Gh. × Porosity Vignettes-0.21 (-0.43, -0.003)-0.20 (-0.41, 0.01)
p = 0.05p = 0.06
Country: Th. × Porosity Vignettes-0.03 (-0.29, 0.23)-0.02 (-0.28, 0.23)
p = 0.82p = 0.87
Country: Ch. × Porosity Vignettes-0.09 (-0.36, 0.18)-0.06 (-0.34, 0.21)
p = 0.52p = 0.64
Country: Va. × Porosity Vignettes0.44 (0.02, 0.87)0.40 (-0.02, 0.83)
p = 0.05p = 0.07
Country: Gh. × Altered states of consciousness (b)0.03 (-0.15, 0.20)0.07 (-0.14, 0.29)0.09 (-0.13, 0.30)
p = 0.76p = 0.52p = 0.44
Country: Th. × Altered states of consciousness (b)-0.16 (-0.35, 0.04)-0.16 (-0.37, 0.05)-0.15 (-0.36, 0.05)
p = 0.12p = 0.13p = 0.15
Country: Ch. × Altered states of consciousness (b)0.08 (-0.11, 0.28)0.06 (-0.23, 0.34)0.05 (-0.23, 0.34)
p = 0.41p = 0.71p = 0.72
Country: Va. × Altered states of consciousness (b)0.11 (-0.10, 0.31)0.06 (-0.15, 0.28)0.03 (-0.19, 0.25)
p = 0.33p = 0.58p = 0.79
Country: Gh. × Site: rural × Religion: CC-0.12 (-0.29, 0.06)-0.15 (-0.32, 0.02)-0.17 (-0.34, 0.01)
p = 0.19p = 0.09p = 0.06
Country: Th. × Site: rural × Religion: CC0.27 (0.06, 0.48)0.23 (0.02, 0.44)0.24 (0.03, 0.46)
p = 0.02p = 0.04p = 0.03
Country: Ch. × Site: rural × Religion: CC-0.12 (-0.31, 0.08)-0.11 (-0.31, 0.09)-0.11 (-0.30, 0.09)
p = 0.24p = 0.28p = 0.28
Country: Va. × Site: rural × Religion: CC-0.02 (-0.24, 0.19)0.03 (-0.19, 0.25)0.04 (-0.18, 0.26)
p = 0.84p = 0.77p = 0.75
Country: Gh. × Porosity Vignettes:Altered states of consciousness (b)-0.09 (-0.32, 0.14)-0.11 (-0.33, 0.12)
p = 0.44p = 0.36
Country: Th. × Porosity Vignettes:Altered states of consciousness (b)-0.16 (-0.50, 0.19)-0.20 (-0.55, 0.14)
p = 0.38p = 0.25
Country: Ch. × Porosity Vignettes:Altered states of consciousness (b)-0.02 (-0.30, 0.26)-0.004 (-0.28, 0.28)
p = 0.88p = 0.98
Country: Va. × Porosity Vignettes:Altered states of consciousness (b)0.29 (-0.25, 0.83)0.32 (-0.22, 0.86)
p = 0.30p = 0.25
Constant-0.01 (-0.12, 0.10)-0.01 (-0.12, 0.10)0.02 (-0.08, 0.11)0.03 (-0.09, 0.15)0.06 (-0.07, 0.20)
p = 0.83p = 0.85p = 0.73p = 0.63p = 0.37
Observations301297301297293
R20.030.140.420.470.49
Adjusted R20.030.130.370.410.42
Residual Std. Error0.97 (df = 299)0.92 (df = 293)0.78 (df = 276)0.76 (df = 262)0.76 (df = 255)
F Statistic10.32*** (df = 1; 299)15.71*** (df = 3; 293)8.46*** (df = 24; 276)6.93*** (df = 34; 262)6.69*** (df = 37; 255)

Study 3

Reliability

# reliability
bind_rows(MR1 = summary(alpha(d_all_abs %>%
                                rownames_to_column("subject_id") %>% 
                                filter(grepl("s3_", subject_id)) %>%
                                column_to_rownames("subject_id") %>%
                                select(d_all_all_scales %>% 
                                         filter(factor == "MR1") %>% 
                                         pull(question)))),
          MR2 = summary(alpha(d_all_abs %>%
                                rownames_to_column("subject_id") %>% 
                                filter(grepl("s3_", subject_id)) %>%
                                column_to_rownames("subject_id") %>%
                                select(d_all_all_scales %>% 
                                         filter(factor == "MR2") %>% 
                                         pull(question)))),
          MR3 = summary(alpha(d_all_abs %>%
                                rownames_to_column("subject_id") %>% 
                                filter(grepl("s3_", subject_id)) %>%
                                column_to_rownames("subject_id") %>%
                                select(d_all_all_scales %>% 
                                         filter(factor == "MR3") %>% 
                                         pull(question)))),
          MR4 = summary(alpha(d_all_abs %>%
                                rownames_to_column("subject_id") %>% 
                                filter(grepl("s3_", subject_id)) %>%
                                column_to_rownames("subject_id") %>%
                                select(d_all_all_scales %>% 
                                         filter(factor == "MR4") %>% 
                                         pull(question)))),
          MR5 = summary(alpha(d_all_abs %>%
                                rownames_to_column("subject_id") %>% 
                                filter(grepl("s3_", subject_id)) %>%
                                column_to_rownames("subject_id") %>%
                                select(d_all_all_scales %>% 
                                         filter(factor == "MR5") %>% 
                                         pull(question)))),
          MR6 = summary(alpha(d_all_abs %>%
                                rownames_to_column("subject_id") %>% 
                                filter(grepl("s3_", subject_id)) %>%
                                column_to_rownames("subject_id") %>%
                                select(d_all_all_scales %>% 
                                         filter(factor == "MR6") %>% 
                                         pull(question)))))

Reliability analysis   

Reliability analysis   

Reliability analysis   

Reliability analysis   

Reliability analysis   

Reliability analysis

Relationships (analog to Table S32)

d3_abs_subscales <- d3_abs %>%
  rownames_to_column("subject_id") %>%
  gather(question, response, -subject_id) %>%
  mutate(question = gsub("_abs", "", question)) %>%
  left_join(d_all_all_scales %>% distinct(question, factor)) %>% 
  filter(!is.na(factor)) %>%
  mutate(factorlab = recode_factor(factor,
                                   "MR1" = "Synesthesia",
                                   "MR2" = "Aesthetic involvement in nature",
                                   "MR3" = "Altered states of consciousness (a)",
                                   "MR4" = "Imaginative involvement",
                                   "MR5" = "Sensed presence",
                                   "MR6" = "Altered states of consciousness (b)")) %>% 
  group_by(subject_id, factor, factorlab) %>%
  summarise(score = mean(response, na.rm = T)) %>%
  ungroup() %>%
  mutate(subject_id = as.numeric(subject_id)) %>%
  left_join(d3 %>% distinct(subject_id, country)) %>%
  group_by(country, factor, factorlab) %>%
  mutate(score = scale(score)) %>%
  ungroup() %>%
  select(-country, -factor) %>%
  spread(factorlab, score) %>%
  mutate_all(as.numeric) %>%
  left_join(d3_fortab)
Joining, by = "question"
`summarise()` has grouped output by 'subject_id', 'factor'. You can override using the `.groups` argument.
Joining, by = "subject_id"
Joining, by = "subject_id"
r103_spev_MR1 <- lm(`Spiritual Events` ~ `Synesthesia`,
                    data = d3_abs_subscales)
r103_spev_MR2 <- lm(`Spiritual Events` ~ `Aesthetic involvement in nature`,
                    data = d3_abs_subscales)
r103_spev_MR3 <- lm(`Spiritual Events` ~ `Altered states of consciousness (a)`,
                    data = d3_abs_subscales)
r103_spev_MR4 <- lm(`Spiritual Events` ~ `Imaginative involvement`,
                    data = d3_abs_subscales)
r103_spev_MR5 <- lm(`Spiritual Events` ~ `Sensed presence`,
                    data = d3_abs_subscales)
r103_spev_MR6 <- lm(`Spiritual Events` ~ `Altered states of consciousness (b)`,
                    data = d3_abs_subscales)

r104_spev_MR1 <- lm(`Spiritual Events` ~ Country * `Synesthesia`,
                    data = d3_abs_subscales)
r104_spev_MR2 <- lm(`Spiritual Events` ~ Country * `Aesthetic involvement in nature`,
                    data = d3_abs_subscales)
r104_spev_MR3 <- lm(`Spiritual Events` ~ Country * `Altered states of consciousness (a)`,
                    data = d3_abs_subscales)
r104_spev_MR4 <- lm(`Spiritual Events` ~ Country * `Imaginative involvement`,
                    data = d3_abs_subscales)
r104_spev_MR5 <- lm(`Spiritual Events` ~ Country * `Sensed presence`,
                    data = d3_abs_subscales)
r104_spev_MR6 <- lm(`Spiritual Events` ~ Country * `Altered states of consciousness (b)`,
                    data = d3_abs_subscales)

r105_spev_MR1 <- lm(`Spiritual Events` ~ Country * `Synesthesia`
                    + Gender + Age,  
                    data = d3_abs_subscales)
r105_spev_MR2 <- lm(`Spiritual Events` ~ Country * `Aesthetic involvement in nature`
                    + Gender + Age,  
                    data = d3_abs_subscales)
r105_spev_MR3 <- lm(`Spiritual Events` ~ Country * `Altered states of consciousness (a)`
                    + Gender + Age,  
                    data = d3_abs_subscales)
r105_spev_MR4 <- lm(`Spiritual Events` ~ Country * `Imaginative involvement`
                    + Gender + Age,  
                    data = d3_abs_subscales)
r105_spev_MR5 <- lm(`Spiritual Events` ~ Country * `Sensed presence`
                    + Gender + Age,  
                    data = d3_abs_subscales)
r105_spev_MR6 <- lm(`Spiritual Events` ~ Country * `Altered states of consciousness (b)`
                    + Gender + Age,  
                    data = d3_abs_subscales)
r103_dse_MR1 <- lm(`Daily Spiritual Experiences` ~ `Synesthesia`,
                    data = d3_abs_subscales)
r103_dse_MR2 <- lm(`Daily Spiritual Experiences` ~ `Aesthetic involvement in nature`,
                    data = d3_abs_subscales)
r103_dse_MR3 <- lm(`Daily Spiritual Experiences` ~ `Altered states of consciousness (a)`,
                    data = d3_abs_subscales)
r103_dse_MR4 <- lm(`Daily Spiritual Experiences` ~ `Imaginative involvement`,
                    data = d3_abs_subscales)
r103_dse_MR5 <- lm(`Daily Spiritual Experiences` ~ `Sensed presence`,
                    data = d3_abs_subscales)
r103_dse_MR6 <- lm(`Daily Spiritual Experiences` ~ `Altered states of consciousness (b)`,
                    data = d3_abs_subscales)

r104_dse_MR1 <- lm(`Daily Spiritual Experiences` ~ Country * `Synesthesia`,
                    data = d3_abs_subscales)
r104_dse_MR2 <- lm(`Daily Spiritual Experiences` ~ Country * `Aesthetic involvement in nature`,
                    data = d3_abs_subscales)
r104_dse_MR3 <- lm(`Daily Spiritual Experiences` ~ Country * `Altered states of consciousness (a)`,
                    data = d3_abs_subscales)
r104_dse_MR4 <- lm(`Daily Spiritual Experiences` ~ Country * `Imaginative involvement`,
                    data = d3_abs_subscales)
r104_dse_MR5 <- lm(`Daily Spiritual Experiences` ~ Country * `Sensed presence`,
                    data = d3_abs_subscales)
r104_dse_MR6 <- lm(`Daily Spiritual Experiences` ~ Country * `Altered states of consciousness (b)`,
                    data = d3_abs_subscales)

r105_dse_MR1 <- lm(`Daily Spiritual Experiences` ~ Country * `Synesthesia`
                    + Gender + Age,  
                    data = d3_abs_subscales)
r105_dse_MR2 <- lm(`Daily Spiritual Experiences` ~ Country * `Aesthetic involvement in nature`
                    + Gender + Age,  
                    data = d3_abs_subscales)
r105_dse_MR3 <- lm(`Daily Spiritual Experiences` ~ Country * `Altered states of consciousness (a)`
                    + Gender + Age,  
                    data = d3_abs_subscales)
r105_dse_MR4 <- lm(`Daily Spiritual Experiences` ~ Country * `Imaginative involvement`
                    + Gender + Age,  
                    data = d3_abs_subscales)
r105_dse_MR5 <- lm(`Daily Spiritual Experiences` ~ Country * `Sensed presence`
                    + Gender + Age,  
                    data = d3_abs_subscales)
r105_dse_MR6 <- lm(`Daily Spiritual Experiences` ~ Country * `Altered states of consciousness (b)`
                    + Gender + Age,  
                    data = d3_abs_subscales)
table_s32_subscales <- stargazer(
  r103_spev_MR1, r103_spev_MR2, r103_spev_MR3, r103_spev_MR4, r103_spev_MR5, r103_spev_MR6,  
  r104_spev_MR1, r104_spev_MR2, r104_spev_MR3, r104_spev_MR4, r104_spev_MR5, r104_spev_MR6,  
  r105_spev_MR1, r105_spev_MR2, r105_spev_MR3, r105_spev_MR4, r105_spev_MR5, r105_spev_MR6,  
  r103_dse_MR1, r103_dse_MR2, r103_dse_MR3, r103_dse_MR4, r103_dse_MR5, r103_dse_MR6,  
  r104_dse_MR1, r104_dse_MR2, r104_dse_MR3, r104_dse_MR4, r104_dse_MR5, r104_dse_MR6,  
  r105_dse_MR1, r105_dse_MR2, r105_dse_MR3, r105_dse_MR4, r105_dse_MR5, r105_dse_MR6,  
                       type = "html",
                       ci = T,
                       report = c("vcsp"),
                       digits = 2,
                       digits.extra = 1000,
                       omit.table.layout = "n",
                       order = c("^`Synesthesia`$",
                                 "^`Aesthetic involvement in nature`$",
                                 "^`Altered states of consciousness \\(a\\)`$",
                                 "^`Imaginative involvement`$",
                                 "^`Sensed presence`$",
                                 "^`Altered states of consciousness \\(b\\)`$"),
                       # dep.var.labels.include = F,
                       dep.var.caption = "",
                       single.row = T)
cat(sub_interact_fun(table_s32_subscales), 
    file = "./stargazer_tables/table_s32_subscales.html")
# htmltools::includeHTML("./stargazer_tables/table_s32_subscales.html")
table_s32_subscales_MR1 <- stargazer(r103_spev_MR1, r104_spev_MR1, r105_spev_MR1, 
                                     r103_dse_MR1, r104_dse_MR1, r105_dse_MR1, 
                                     type = "html",
                                     ci = T,
                                     report = c("vcsp"),
                                     digits = 2,
                                     digits.extra = 1000,
                                     omit.table.layout = "n",
                                     order = c("^Synesthesia$",
                                               "^`Aesthetic involvement in nature`$",
                                               "^`Altered states of consciousness \\(a\\)`$",
                                               "^`Imaginative involvement`$",
                                               "^`Sensed presence`$",
                                               "^`Altered states of consciousness \\(b\\)`$"),
                                     # dep.var.labels.include = F,
                                     dep.var.caption = "",
                                     single.row = T)
cat(sub_interact_fun(table_s32_subscales_MR1), 
    file = "./stargazer_tables/table_s32_subscales_MR1.html")
htmltools::includeHTML("./stargazer_tables/table_s32_subscales_MR1.html")
Spiritual EventsDaily Spiritual Experiences
(1)(2)(3)(4)(5)(6)
Synesthesia0.20 (0.12, 0.29)0.20 (0.14, 0.27)0.20 (0.13, 0.26)0.09 (0.004, 0.18)0.09 (0.03, 0.15)0.08 (0.02, 0.14)
p = 0.000005p = 0.00p = 0.00000002p = 0.05p = 0.01p = 0.01
Country: Gh.0.89 (0.75, 1.02)0.90 (0.76, 1.03)0.87 (0.75, 1.00)0.89 (0.76, 1.01)
p = 0.00p = 0.00p = 0.00p = 0.00
Country: Th.-0.50 (-0.63, -0.37)-0.48 (-0.62, -0.35)-0.18 (-0.30, -0.06)-0.18 (-0.31, -0.06)
p = 0.00p = 0.00p = 0.003p = 0.005
Country: Ch.-0.67 (-0.80, -0.54)-0.68 (-0.81, -0.54)-1.13 (-1.25, -1.01)-1.14 (-1.26, -1.02)
p = 0.00p = 0.00p = 0.00p = 0.00
Country: Va.0.59 (0.46, 0.72)0.52 (0.37, 0.68)0.66 (0.53, 0.78)0.59 (0.45, 0.73)
p = 0.00p = 0.00p = 0.00p = 0.00
Gender: male-0.02 (-0.10, 0.05)-0.07 (-0.13, -0.004)
p = 0.50p = 0.04
Age0.08 (-0.01, 0.16)0.09 (0.01, 0.16)
p = 0.08p = 0.03
Country: Gh. × Synesthesia0.14 (0.002, 0.27)0.14 (0.005, 0.28)-0.09 (-0.22, 0.03)-0.09 (-0.22, 0.03)
p = 0.05p = 0.05p = 0.15p = 0.16
Country: Th. × Synesthesia-0.04 (-0.17, 0.09)-0.04 (-0.17, 0.10)0.10 (-0.02, 0.22)0.10 (-0.02, 0.22)
p = 0.58p = 0.60p = 0.10p = 0.10
Country: Ch. × Synesthesia-0.15 (-0.28, -0.02)-0.15 (-0.28, -0.01)-0.08 (-0.20, 0.05)-0.08 (-0.20, 0.04)
p = 0.03p = 0.04p = 0.23p = 0.20
Country: Va. × Synesthesia0.07 (-0.07, 0.20)0.05 (-0.09, 0.19)0.07 (-0.05, 0.19)0.05 (-0.07, 0.18)
p = 0.32p = 0.47p = 0.28p = 0.39
Constant0.00 (-0.08, 0.08)0.03 (-0.04, 0.09)0.02 (-0.04, 0.09)0.00 (-0.09, 0.09)0.02 (-0.04, 0.08)0.01 (-0.05, 0.07)
p = 1.00p = 0.45p = 0.51p = 1.00p = 0.51p = 0.71
Observations519519513519519513
R20.040.430.430.010.510.52
Adjusted R20.040.420.420.010.510.51
Residual Std. Error0.98 (df = 517)0.76 (df = 509)0.77 (df = 501)1.00 (df = 517)0.70 (df = 509)0.70 (df = 501)
F Statistic21.56*** (df = 1; 517)42.08*** (df = 9; 509)34.12*** (df = 11; 501)4.16** (df = 1; 517)59.76*** (df = 9; 509)49.64*** (df = 11; 501)
table_s32_subscales_MR2 <- stargazer(r103_spev_MR2, r104_spev_MR2, r105_spev_MR2, 
                                     r103_dse_MR2, r104_dse_MR2, r105_dse_MR2, 
                                     type = "html",
                                     ci = T,
                                     report = c("vcsp"),
                                     digits = 2,
                                     digits.extra = 1000,
                                     omit.table.layout = "n",
                                     order = c("^Synesthesia$",
                                               "^`Aesthetic involvement in nature`$",
                                               "^`Altered states of consciousness \\(a\\)`$",
                                               "^`Imaginative involvement`$",
                                               "^`Sensed presence`$",
                                               "^`Altered states of consciousness \\(b\\)`$"),
                                     # dep.var.labels.include = F,
                                     dep.var.caption = "",
                                     single.row = T)
cat(sub_interact_fun(table_s32_subscales_MR2), 
    file = "./stargazer_tables/table_s32_subscales_MR2.html")
htmltools::includeHTML("./stargazer_tables/table_s32_subscales_MR2.html")
Spiritual EventsDaily Spiritual Experiences
(1)(2)(3)(4)(5)(6)
Aesthetic involvement in nature0.14 (0.06, 0.23)0.15 (0.08, 0.21)0.15 (0.08, 0.21)0.14 (0.05, 0.23)0.14 (0.08, 0.20)0.13 (0.07, 0.19)
p = 0.002p = 0.00002p = 0.00003p = 0.002p = 0.00001p = 0.00002
Country: Gh.0.89 (0.75, 1.02)0.90 (0.76, 1.04)0.87 (0.75, 1.00)0.89 (0.76, 1.01)
p = 0.00p = 0.00p = 0.00p = 0.00
Country: Th.-0.50 (-0.63, -0.37)-0.48 (-0.61, -0.34)-0.18 (-0.30, -0.06)-0.17 (-0.30, -0.05)
p = 0.00p = 0.00p = 0.003p = 0.01
Country: Ch.-0.67 (-0.80, -0.54)-0.68 (-0.81, -0.54)-1.13 (-1.25, -1.01)-1.14 (-1.26, -1.02)
p = 0.00p = 0.00p = 0.00p = 0.00
Country: Va.0.59 (0.45, 0.72)0.50 (0.34, 0.66)0.66 (0.53, 0.78)0.58 (0.44, 0.72)
p = 0.00p = 0.00p = 0.00p = 0.00
Gender: male-0.02 (-0.09, 0.05)-0.05 (-0.12, 0.01)
p = 0.62p = 0.11
Age0.09 (0.01, 0.17)0.09 (0.02, 0.16)
p = 0.04p = 0.02
Country: Gh. × Aesthetic involvement in nature0.21 (0.08, 0.35)0.22 (0.08, 0.36)-0.01 (-0.13, 0.12)0.001 (-0.12, 0.12)
p = 0.003p = 0.002p = 0.90p = 0.99
Country: Th. × Aesthetic involvement in nature-0.07 (-0.20, 0.06)-0.07 (-0.20, 0.06)0.05 (-0.07, 0.17)0.05 (-0.07, 0.16)
p = 0.33p = 0.31p = 0.43p = 0.45
Country: Ch. × Aesthetic involvement in nature-0.08 (-0.21, 0.06)-0.08 (-0.21, 0.06)-0.06 (-0.18, 0.06)-0.06 (-0.18, 0.06)
p = 0.26p = 0.27p = 0.33p = 0.31
Country: Va. × Aesthetic involvement in nature-0.05 (-0.18, 0.09)-0.05 (-0.19, 0.09)-0.07 (-0.19, 0.05)-0.07 (-0.19, 0.06)
p = 0.50p = 0.48p = 0.25p = 0.29
Constant0.00 (-0.09, 0.09)0.03 (-0.04, 0.09)0.02 (-0.04, 0.09)0.00 (-0.09, 0.09)0.02 (-0.04, 0.08)0.01 (-0.05, 0.07)
p = 1.00p = 0.46p = 0.51p = 1.00p = 0.51p = 0.71
Observations519519513519519513
R20.020.410.420.020.520.53
Adjusted R20.020.400.400.020.510.52
Residual Std. Error0.99 (df = 517)0.78 (df = 509)0.77 (df = 501)0.99 (df = 517)0.70 (df = 509)0.69 (df = 501)
F Statistic10.89*** (df = 1; 517)39.16*** (df = 9; 509)32.34*** (df = 11; 501)10.17*** (df = 1; 517)62.06*** (df = 9; 509)51.48*** (df = 11; 501)
table_s32_subscales_MR3 <- stargazer(r103_spev_MR3, r104_spev_MR3, r105_spev_MR3, 
                                     r103_dse_MR3, r104_dse_MR3, r105_dse_MR3, 
                                     type = "html",
                                     ci = T,
                                     report = c("vcsp"),
                                     digits = 2,
                                     digits.extra = 1000,
                                     omit.table.layout = "n",
                                     order = c("^Synesthesia$",
                                               "^`Aesthetic involvement in nature`$",
                                               "^`Altered states of consciousness \\(a\\)`$",
                                               "^`Imaginative involvement`$",
                                               "^`Sensed presence`$",
                                               "^`Altered states of consciousness \\(b\\)`$"),
                                     # dep.var.labels.include = F,
                                     dep.var.caption = "",
                                     single.row = T)
cat(sub_interact_fun(table_s32_subscales_MR3), 
    file = "./stargazer_tables/table_s32_subscales_MR3.html")
htmltools::includeHTML("./stargazer_tables/table_s32_subscales_MR3.html")
Spiritual EventsDaily Spiritual Experiences
(1)(2)(3)(4)(5)(6)
Altered states of consciousness (a)0.20 (0.12, 0.29)0.21 (0.14, 0.27)0.21 (0.15, 0.28)0.13 (0.04, 0.21)0.13 (0.07, 0.19)0.13 (0.07, 0.19)
p = 0.000005p = 0.00p = 0.00p = 0.005p = 0.00005p = 0.00004
Country: Gh.0.89 (0.75, 1.02)0.90 (0.76, 1.03)0.87 (0.75, 1.00)0.89 (0.76, 1.01)
p = 0.00p = 0.00p = 0.00p = 0.00
Country: Th.-0.50 (-0.63, -0.38)-0.48 (-0.61, -0.34)-0.18 (-0.30, -0.06)-0.17 (-0.30, -0.05)
p = 0.00p = 0.00p = 0.003p = 0.01
Country: Ch.-0.67 (-0.80, -0.54)-0.67 (-0.81, -0.54)-1.13 (-1.25, -1.01)-1.14 (-1.26, -1.02)
p = 0.00p = 0.00p = 0.00p = 0.00
Country: Va.0.59 (0.46, 0.72)0.51 (0.36, 0.66)0.66 (0.53, 0.78)0.58 (0.44, 0.72)
p = 0.00p = 0.00p = 0.00p = 0.00
Gender: male-0.01 (-0.08, 0.06)-0.06 (-0.12, 0.01)
p = 0.82p = 0.10
Age0.07 (-0.01, 0.15)0.09 (0.02, 0.16)
p = 0.08p = 0.02
Country: Gh. × Altered states of consciousness (a)0.19 (0.06, 0.33)0.18 (0.04, 0.31)0.06 (-0.07, 0.18)0.05 (-0.08, 0.17)
p = 0.01p = 0.01p = 0.37p = 0.46
Country: Th. × Altered states of consciousness (a)-0.18 (-0.31, -0.05)-0.17 (-0.30, -0.04)-0.07 (-0.18, 0.05)-0.06 (-0.18, 0.05)
p = 0.01p = 0.01p = 0.27p = 0.30
Country: Ch. × Altered states of consciousness (a)-0.10 (-0.23, 0.03)-0.09 (-0.23, 0.04)-0.05 (-0.17, 0.07)-0.05 (-0.17, 0.07)
p = 0.16p = 0.17p = 0.40p = 0.45
Country: Va. × Altered states of consciousness (a)0.10 (-0.03, 0.24)0.11 (-0.02, 0.25)-0.02 (-0.14, 0.10)-0.02 (-0.14, 0.10)
p = 0.14p = 0.11p = 0.78p = 0.75
Constant0.00 (-0.08, 0.08)0.03 (-0.04, 0.09)0.02 (-0.04, 0.09)0.00 (-0.09, 0.09)0.02 (-0.04, 0.08)0.01 (-0.05, 0.07)
p = 1.00p = 0.45p = 0.52p = 1.00p = 0.51p = 0.73
Observations519519513519519513
R20.040.430.440.020.520.53
Adjusted R20.040.420.430.010.510.52
Residual Std. Error0.98 (df = 517)0.76 (df = 509)0.76 (df = 501)0.99 (df = 517)0.70 (df = 509)0.70 (df = 501)
F Statistic21.63*** (df = 1; 517)43.53*** (df = 9; 509)35.90*** (df = 11; 501)8.26*** (df = 1; 517)61.05*** (df = 9; 509)50.93*** (df = 11; 501)
table_s32_subscales_MR4 <- stargazer(r103_spev_MR4, r104_spev_MR4, r105_spev_MR4, 
                                     r103_dse_MR4, r104_dse_MR4, r105_dse_MR4, 
                                     type = "html",
                                     ci = T,
                                     report = c("vcsp"),
                                     digits = 2,
                                     digits.extra = 1000,
                                     omit.table.layout = "n",
                                     order = c("^Synesthesia$",
                                               "^`Aesthetic involvement in nature`$",
                                               "^`Altered states of consciousness \\(a\\)`$",
                                               "^`Imaginative involvement`$",
                                               "^`Sensed presence`$",
                                               "^`Altered states of consciousness \\(b\\)`$"),
                                     # dep.var.labels.include = F,
                                     dep.var.caption = "",
                                     single.row = T)
cat(sub_interact_fun(table_s32_subscales_MR4), 
    file = "./stargazer_tables/table_s32_subscales_MR4.html")
htmltools::includeHTML("./stargazer_tables/table_s32_subscales_MR4.html")
Spiritual EventsDaily Spiritual Experiences
(1)(2)(3)(4)(5)(6)
Imaginative involvement0.15 (0.07, 0.24)0.16 (0.09, 0.23)0.16 (0.09, 0.22)0.09 (0.004, 0.18)0.09 (0.03, 0.15)0.09 (0.03, 0.15)
p = 0.0005p = 0.000004p = 0.00001p = 0.05p = 0.004p = 0.005
Country: Gh.0.89 (0.75, 1.02)0.90 (0.76, 1.03)0.87 (0.75, 1.00)0.89 (0.76, 1.01)
p = 0.00p = 0.00p = 0.00p = 0.00
Country: Th.-0.50 (-0.63, -0.37)-0.48 (-0.61, -0.34)-0.18 (-0.30, -0.06)-0.17 (-0.30, -0.05)
p = 0.00p = 0.00p = 0.003p = 0.01
Country: Ch.-0.67 (-0.80, -0.54)-0.68 (-0.81, -0.54)-1.13 (-1.25, -1.01)-1.14 (-1.26, -1.02)
p = 0.00p = 0.00p = 0.00p = 0.00
Country: Va.0.59 (0.45, 0.72)0.50 (0.34, 0.66)0.66 (0.53, 0.78)0.57 (0.43, 0.71)
p = 0.00p = 0.00p = 0.00p = 0.00
Gender: male-0.02 (-0.10, 0.05)-0.07 (-0.14, -0.01)
p = 0.52p = 0.03
Age0.09 (0.01, 0.18)0.11 (0.04, 0.18)
p = 0.03p = 0.004
Country: Gh. × Imaginative involvement0.17 (0.03, 0.31)0.17 (0.03, 0.31)0.10 (-0.02, 0.23)0.10 (-0.02, 0.23)
p = 0.02p = 0.02p = 0.11p = 0.11
Country: Th. × Imaginative involvement-0.16 (-0.29, -0.03)-0.15 (-0.28, -0.02)0.01 (-0.11, 0.13)0.01 (-0.11, 0.13)
p = 0.02p = 0.03p = 0.84p = 0.85
Country: Ch. × Imaginative involvement0.03 (-0.10, 0.16)0.05 (-0.09, 0.18)0.07 (-0.05, 0.19)0.09 (-0.03, 0.21)
p = 0.66p = 0.51p = 0.26p = 0.14
Country: Va. × Imaginative involvement-0.02 (-0.15, 0.12)-0.04 (-0.18, 0.10)-0.13 (-0.25, -0.01)-0.17 (-0.29, -0.04)
p = 0.83p = 0.56p = 0.04p = 0.01
Constant0.00 (-0.09, 0.09)0.03 (-0.04, 0.09)0.02 (-0.04, 0.09)0.00 (-0.09, 0.09)0.02 (-0.04, 0.08)0.01 (-0.05, 0.07)
p = 1.00p = 0.46p = 0.52p = 1.00p = 0.51p = 0.75
Observations519519513519519513
R20.020.410.420.010.510.53
Adjusted R20.020.400.400.010.510.52
Residual Std. Error0.99 (df = 517)0.77 (df = 509)0.77 (df = 501)1.00 (df = 517)0.70 (df = 509)0.70 (df = 501)
F Statistic12.62*** (df = 1; 517)39.63*** (df = 9; 509)32.55*** (df = 11; 501)4.19** (df = 1; 517)60.00*** (df = 9; 509)50.51*** (df = 11; 501)
table_s32_subscales_MR5 <- stargazer(r103_spev_MR5, r104_spev_MR5, r105_spev_MR5, 
                                     r103_dse_MR5, r104_dse_MR5, r105_dse_MR5, 
                                     type = "html",
                                     ci = T,
                                     report = c("vcsp"),
                                     digits = 2,
                                     digits.extra = 1000,
                                     omit.table.layout = "n",
                                     order = c("^Synesthesia$",
                                               "^`Aesthetic involvement in nature`$",
                                               "^`Altered states of consciousness \\(a\\)`$",
                                               "^`Imaginative involvement`$",
                                               "^`Sensed presence`$",
                                               "^`Altered states of consciousness \\(b\\)`$"),
                                     # dep.var.labels.include = F,
                                     dep.var.caption = "",
                                     single.row = T)
cat(sub_interact_fun(table_s32_subscales_MR5), 
    file = "./stargazer_tables/table_s32_subscales_MR5.html")
htmltools::includeHTML("./stargazer_tables/table_s32_subscales_MR5.html")
Spiritual EventsDaily Spiritual Experiences
(1)(2)(3)(4)(5)(6)
Sensed presence0.29 (0.21, 0.37)0.29 (0.23, 0.36)0.29 (0.23, 0.36)0.18 (0.09, 0.26)0.18 (0.12, 0.24)0.18 (0.12, 0.24)
p = 0.00p = 0.00p = 0.00p = 0.00005p = 0.00p = 0.00
Country: Gh.0.89 (0.76, 1.02)0.90 (0.77, 1.03)0.87 (0.75, 1.00)0.89 (0.77, 1.01)
p = 0.00p = 0.00p = 0.00p = 0.00
Country: Th.-0.50 (-0.63, -0.38)-0.48 (-0.61, -0.35)-0.18 (-0.29, -0.06)-0.17 (-0.29, -0.05)
p = 0.00p = 0.00p = 0.003p = 0.01
Country: Ch.-0.67 (-0.80, -0.54)-0.68 (-0.80, -0.55)-1.13 (-1.25, -1.01)-1.14 (-1.25, -1.02)
p = 0.00p = 0.00p = 0.00p = 0.00
Country: Va.0.59 (0.46, 0.72)0.51 (0.36, 0.66)0.66 (0.54, 0.78)0.58 (0.44, 0.72)
p = 0.00p = 0.00p = 0.00p = 0.00
Gender: male-0.02 (-0.09, 0.05)-0.06 (-0.12, 0.01)
p = 0.60p = 0.08
Age0.08 (0.001, 0.16)0.09 (0.02, 0.16)
p = 0.05p = 0.02
Country: Gh. × Sensed presence0.14 (0.01, 0.27)0.13 (-0.001, 0.26)-0.04 (-0.16, 0.09)-0.05 (-0.17, 0.08)
p = 0.04p = 0.06p = 0.56p = 0.47
Country: Th. × Sensed presence-0.10 (-0.22, 0.03)-0.09 (-0.22, 0.03)0.04 (-0.07, 0.16)0.04 (-0.07, 0.16)
p = 0.13p = 0.15p = 0.46p = 0.45
Country: Ch. × Sensed presence-0.09 (-0.22, 0.04)-0.09 (-0.22, 0.04)0.004 (-0.12, 0.12)0.01 (-0.11, 0.12)
p = 0.17p = 0.18p = 0.96p = 0.92
Country: Va. × Sensed presence0.02 (-0.11, 0.15)0.02 (-0.11, 0.15)-0.10 (-0.22, 0.02)-0.10 (-0.22, 0.02)
p = 0.80p = 0.78p = 0.11p = 0.12
Constant0.00 (-0.08, 0.08)0.03 (-0.04, 0.09)0.02 (-0.04, 0.09)0.00 (-0.08, 0.08)0.02 (-0.04, 0.08)0.01 (-0.05, 0.07)
p = 1.00p = 0.44p = 0.51p = 1.00p = 0.50p = 0.72
Observations519519513519519513
R20.080.470.470.030.540.55
Adjusted R20.080.460.460.030.530.54
Residual Std. Error0.96 (df = 517)0.74 (df = 509)0.74 (df = 501)0.98 (df = 517)0.69 (df = 509)0.68 (df = 501)
F Statistic46.39*** (df = 1; 517)49.59*** (df = 9; 509)40.86*** (df = 11; 501)17.09*** (df = 1; 517)65.34*** (df = 9; 509)54.80*** (df = 11; 501)
table_s32_subscales_MR6 <- stargazer(r103_spev_MR6, r104_spev_MR6, r105_spev_MR6, 
                                     r103_dse_MR6, r104_dse_MR6, r105_dse_MR6, 
                                     type = "html",
                                     ci = T,
                                     report = c("vcsp"),
                                     digits = 2,
                                     digits.extra = 1000,
                                     omit.table.layout = "n",
                                     order = c("^Synesthesia$",
                                               "^`Aesthetic involvement in nature`$",
                                               "^`Altered states of consciousness \\(a\\)`$",
                                               "^`Imaginative involvement`$",
                                               "^`Sensed presence`$",
                                               "^`Altered states of consciousness \\(b\\)`$"),
                                     # dep.var.labels.include = F,
                                     dep.var.caption = "",
                                     single.row = T)
cat(sub_interact_fun(table_s32_subscales_MR6), 
    file = "./stargazer_tables/table_s32_subscales_MR6.html")
htmltools::includeHTML("./stargazer_tables/table_s32_subscales_MR6.html")
Spiritual EventsDaily Spiritual Experiences
(1)(2)(3)(4)(5)(6)
Altered states of consciousness (b)0.28 (0.20, 0.36)0.28 (0.22, 0.35)0.29 (0.22, 0.35)0.17 (0.09, 0.26)0.17 (0.11, 0.23)0.18 (0.12, 0.24)
p = 0.00p = 0.00p = 0.00p = 0.0001p = 0.00000003p = 0.00
Country: Gh.0.89 (0.76, 1.02)0.90 (0.77, 1.03)0.87 (0.75, 1.00)0.89 (0.77, 1.01)
p = 0.00p = 0.00p = 0.00p = 0.00
Country: Th.-0.50 (-0.63, -0.38)-0.49 (-0.62, -0.36)-0.18 (-0.30, -0.06)-0.18 (-0.31, -0.06)
p = 0.00p = 0.00p = 0.003p = 0.004
Country: Ch.-0.67 (-0.80, -0.54)-0.68 (-0.80, -0.55)-1.13 (-1.25, -1.01)-1.14 (-1.26, -1.02)
p = 0.00p = 0.00p = 0.00p = 0.00
Country: Va.0.59 (0.46, 0.72)0.52 (0.38, 0.67)0.66 (0.54, 0.78)0.59 (0.45, 0.73)
p = 0.00p = 0.00p = 0.00p = 0.00
Gender: male-0.04 (-0.11, 0.03)-0.08 (-0.14, -0.02)
p = 0.26p = 0.02
Age0.07 (-0.003, 0.15)0.09 (0.02, 0.16)
p = 0.06p = 0.02
Country: Gh. × Altered states of consciousness (b)0.10 (-0.03, 0.23)0.10 (-0.03, 0.23)0.03 (-0.09, 0.15)0.02 (-0.10, 0.14)
p = 0.14p = 0.13p = 0.66p = 0.75
Country: Th. × Altered states of consciousness (b)-0.15 (-0.28, -0.03)-0.16 (-0.28, -0.03)0.04 (-0.07, 0.16)0.04 (-0.08, 0.15)
p = 0.02p = 0.02p = 0.47p = 0.55
Country: Ch. × Altered states of consciousness (b)-0.13 (-0.25, 0.002)-0.12 (-0.25, 0.01)-0.06 (-0.18, 0.06)-0.05 (-0.16, 0.07)
p = 0.06p = 0.07p = 0.36p = 0.46
Country: Va. × Altered states of consciousness (b)0.10 (-0.03, 0.23)0.09 (-0.04, 0.22)-0.06 (-0.18, 0.06)-0.07 (-0.19, 0.05)
p = 0.14p = 0.19p = 0.37p = 0.27
Constant0.00 (-0.08, 0.08)0.03 (-0.04, 0.09)0.02 (-0.04, 0.09)0.00 (-0.08, 0.08)0.02 (-0.04, 0.08)0.01 (-0.05, 0.07)
p = 1.00p = 0.44p = 0.47p = 1.00p = 0.51p = 0.71
Observations519519513519519513
R20.080.470.470.030.530.54
Adjusted R20.080.460.460.030.520.53
Residual Std. Error0.96 (df = 517)0.74 (df = 509)0.74 (df = 501)0.99 (df = 517)0.69 (df = 509)0.69 (df = 501)
F Statistic43.67*** (df = 1; 517)49.83*** (df = 9; 509)41.00*** (df = 11; 501)16.06*** (df = 1; 517)64.32*** (df = 9; 509)54.03*** (df = 11; 501)

Study 4

Reliability

# reliability
bind_rows(MR1 = summary(alpha(d_all_abs %>%
                                rownames_to_column("subject_id") %>% 
                                filter(grepl("s4_", subject_id)) %>%
                                column_to_rownames("subject_id") %>%
                                select(d_all_all_scales %>% 
                                         filter(factor == "MR1") %>% 
                                         pull(question)))),
          MR2 = summary(alpha(d_all_abs %>%
                                rownames_to_column("subject_id") %>% 
                                filter(grepl("s4_", subject_id)) %>%
                                column_to_rownames("subject_id") %>%
                                select(d_all_all_scales %>% 
                                         filter(factor == "MR2") %>% 
                                         pull(question)))),
          MR3 = summary(alpha(d_all_abs %>%
                                rownames_to_column("subject_id") %>% 
                                filter(grepl("s4_", subject_id)) %>%
                                column_to_rownames("subject_id") %>%
                                select(d_all_all_scales %>% 
                                         filter(factor == "MR3") %>% 
                                         pull(question)))),
          MR4 = summary(alpha(d_all_abs %>%
                                rownames_to_column("subject_id") %>% 
                                filter(grepl("s4_", subject_id)) %>%
                                column_to_rownames("subject_id") %>%
                                select(d_all_all_scales %>% 
                                         filter(factor == "MR4") %>% 
                                         pull(question)))),
          MR5 = summary(alpha(d_all_abs %>%
                                rownames_to_column("subject_id") %>% 
                                filter(grepl("s4_", subject_id)) %>%
                                column_to_rownames("subject_id") %>%
                                select(d_all_all_scales %>% 
                                         filter(factor == "MR5") %>% 
                                         pull(question)))),
          MR6 = summary(alpha(d_all_abs %>%
                                rownames_to_column("subject_id") %>% 
                                filter(grepl("s4_", subject_id)) %>%
                                column_to_rownames("subject_id") %>%
                                select(d_all_all_scales %>% 
                                         filter(factor == "MR6") %>% 
                                         pull(question)))))

Reliability analysis   

Reliability analysis   

Reliability analysis   

Reliability analysis   

Reliability analysis   

Reliability analysis

Relationships (analog to Tables S40-S41)

d4_abs_subscales <- d_all_abs %>%
  rownames_to_column("subject_id") %>% 
  filter(grepl("s4_", subject_id)) %>%
  gather(question, response, -subject_id) %>%
  left_join(d_all_all_scales %>% distinct(question, factor)) %>% 
  filter(!is.na(factor)) %>%
  mutate(factorlab = recode_factor(factor,
                                   "MR1" = "Synesthesia",
                                   "MR2" = "Aesthetic involvement in nature",
                                   "MR3" = "Altered states of consciousness (a)",
                                   "MR4" = "Imaginative involvement",
                                   "MR5" = "Sensed presence",
                                   "MR6" = "Altered states of consciousness (b)")) %>% 
  group_by(subject_id, factor, factorlab) %>%
  summarise(score = mean(response, na.rm = T)) %>%
  ungroup() %>%
  mutate(subject_id = as.numeric(gsub("s4_", "", subject_id))) %>%
  left_join(d4 %>% distinct(subject_id, country)) %>%
  group_by(country, factor, factorlab) %>%
  mutate(score = scale(score)) %>%
  ungroup() %>%
  select(-country, -factor) %>%
  spread(factorlab, score) %>%
  mutate_all(as.numeric) %>%
  left_join(d4_fortab_long_spirit)
Joining, by = "question"
`summarise()` has grouped output by 'subject_id', 'factor'. You can override using the `.groups` argument.
Joining, by = "subject_id"
Joining, by = "subject_id"

Analog to Table S40

r105_spirit_MR1 <- lmer(Score ~ Scale * Synesthesia
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)

r108_spirit_MR1 <- lmer(Score ~ Scale * Country * Synesthesia
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)

r109_spirit_MR1 <- lmer(Score ~ Scale * Country * `Porosity Vignettes` * Synesthesia
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)

r110_spirit_MR1 <- lmer(Score ~ Scale * Country * `Porosity Scale` * Synesthesia
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)

r111_spirit_MR1 <- lmer(Score ~ Scale * Country * `Porosity Vignettes` * Synesthesia
                        + Gender + Age
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)

r112_spirit_MR1 <- lmer(Score ~ Scale * Country * `Porosity Scale` * Synesthesia
                        + Gender + Age
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)
r105_spirit_MR2 <- lmer(Score ~ Scale * `Aesthetic involvement in nature`
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)

r108_spirit_MR2 <- lmer(Score ~ Scale * Country * `Aesthetic involvement in nature`
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)

r109_spirit_MR2 <- lmer(Score ~ Scale * Country * `Porosity Vignettes` * `Aesthetic involvement in nature`
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)

r110_spirit_MR2 <- lmer(Score ~ Scale * Country * `Porosity Scale` * `Aesthetic involvement in nature`
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)

r111_spirit_MR2 <- lmer(Score ~ Scale * Country * `Porosity Vignettes` * `Aesthetic involvement in nature`
                        + Gender + Age
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)

r112_spirit_MR2 <- lmer(Score ~ Scale * Country * `Porosity Scale` * `Aesthetic involvement in nature`
                        + Gender + Age
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)
r105_spirit_MR3 <- lmer(Score ~ Scale * `Altered states of consciousness (a)`
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)

r108_spirit_MR3 <- lmer(Score ~ Scale * Country * `Altered states of consciousness (a)`
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)

r109_spirit_MR3 <- lmer(Score ~ Scale * Country * `Porosity Vignettes` * `Altered states of consciousness (a)`
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)

r110_spirit_MR3 <- lmer(Score ~ Scale * Country * `Porosity Scale` * `Altered states of consciousness (a)`
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)

r111_spirit_MR3 <- lmer(Score ~ Scale * Country * `Porosity Vignettes` * `Altered states of consciousness (a)`
                        + Gender + Age
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)

r112_spirit_MR3 <- lmer(Score ~ Scale * Country * `Porosity Scale` * `Altered states of consciousness (a)`
                        + Gender + Age
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)
r105_spirit_MR4 <- lmer(Score ~ Scale * `Imaginative involvement`
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)

r108_spirit_MR4 <- lmer(Score ~ Scale * Country * `Imaginative involvement`
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)

r109_spirit_MR4 <- lmer(Score ~ Scale * Country * `Porosity Vignettes` * `Imaginative involvement`
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)

r110_spirit_MR4 <- lmer(Score ~ Scale * Country * `Porosity Scale` * `Imaginative involvement`
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)

r111_spirit_MR4 <- lmer(Score ~ Scale * Country * `Porosity Vignettes` * `Imaginative involvement`
                        + Gender + Age
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)

r112_spirit_MR4 <- lmer(Score ~ Scale * Country * `Porosity Scale` * `Imaginative involvement`
                        + Gender + Age
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)
r105_spirit_MR5 <- lmer(Score ~ Scale * `Sensed presence`
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)

r108_spirit_MR5 <- lmer(Score ~ Scale * Country * `Sensed presence`
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)

r109_spirit_MR5 <- lmer(Score ~ Scale * Country * `Porosity Vignettes` * `Sensed presence`
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)

r110_spirit_MR5 <- lmer(Score ~ Scale * Country * `Porosity Scale` * `Sensed presence`
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)

r111_spirit_MR5 <- lmer(Score ~ Scale * Country * `Porosity Vignettes` * `Sensed presence`
                        + Gender + Age
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)

r112_spirit_MR5 <- lmer(Score ~ Scale * Country * `Porosity Scale` * `Sensed presence`
                        + Gender + Age
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)
r105_spirit_MR6 <- lmer(Score ~ Scale * `Altered states of consciousness (b)`
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)

r108_spirit_MR6 <- lmer(Score ~ Scale * Country * `Altered states of consciousness (b)`
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)

r109_spirit_MR6 <- lmer(Score ~ Scale * Country * `Porosity Vignettes` * `Altered states of consciousness (b)`
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)

r110_spirit_MR6 <- lmer(Score ~ Scale * Country * `Porosity Scale` * `Altered states of consciousness (b)`
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)

r111_spirit_MR6 <- lmer(Score ~ Scale * Country * `Porosity Vignettes` * `Altered states of consciousness (b)`
                        + Gender + Age
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)

r112_spirit_MR6 <- lmer(Score ~ Scale * Country * `Porosity Scale` * `Altered states of consciousness (b)`
                        + Gender + Age
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)
table_s40s41_subscales_MR1 <- stargazer(r105_spirit_MR1, r108_spirit_MR1, 
                                        r109_spirit_MR1, r110_spirit_MR1,
                                        r111_spirit_MR1, r112_spirit_MR1,
                                        type = "html",
                                        ci = T,
                                        report = c("vcsp"),
                                        digits = 2,
                                        digits.extra = 1000,
                                        omit.table.layout = "n",
                                        order = c("^Synesthesia$",
                                                  "^`Porosity Vignettes`$",
                                                  "^`Porosity Scale`$",
                                                  "^`Porosity Vignettes`:Synesthesia$",
                                                  "^`Porosity Scale`:Synesthesia$"),
                                        # dep.var.labels.include = F,
                                        dep.var.caption = "",
                                        single.row = T) 
cat(sub_interact_fun(table_s40s41_subscales_MR1), 
    file = "./stargazer_tables/table_s40s41_subscales_MR1.html")
htmltools::includeHTML("./stargazer_tables/table_s40s41_subscales_MR1.html")
Score
(1)(2)(3)(4)(5)(6)
Synesthesia0.13 (0.05, 0.21)0.13 (0.07, 0.19)0.08 (0.02, 0.15)0.10 (0.02, 0.18)0.07 (0.01, 0.14)0.09 (0.01, 0.17)
p = 0.002p = 0.00001p = 0.02p = 0.02p = 0.04p = 0.03
Porosity Vignettes0.24 (0.17, 0.31)0.25 (0.18, 0.32)
p = 0.00p = 0.00
Porosity Scale0.35 (0.28, 0.42)0.36 (0.28, 0.43)
p = 0.00p = 0.00
Porosity Vignettes:Synesthesia-0.002 (-0.07, 0.07)0.01 (-0.06, 0.08)
p = 0.95p = 0.85
Porosity Scale:Synesthesia-0.05 (-0.12, 0.03)-0.05 (-0.13, 0.02)
p = 0.22p = 0.19
Scale: Spiritual Events-0.001 (-0.04, 0.03)-0.001 (-0.03, 0.03)-0.02 (-0.06, 0.02)-0.01 (-0.06, 0.04)-0.02 (-0.06, 0.02)-0.005 (-0.06, 0.05)
p = 0.95p = 0.98p = 0.33p = 0.75p = 0.37p = 0.86
Country: Gh.0.83 (0.71, 0.94)0.75 (0.61, 0.88)0.63 (0.50, 0.76)0.73 (0.59, 0.86)0.60 (0.46, 0.73)
p = 0.00p = 0.00p = 0.00p = 0.00p = 0.00
Country: Th.-0.29 (-0.40, -0.18)-0.29 (-0.40, -0.17)-0.25 (-0.36, -0.13)-0.29 (-0.41, -0.17)-0.24 (-0.36, -0.12)
p = 0.0000003p = 0.000001p = 0.00004p = 0.000001p = 0.0002
Country: Ch.-0.86 (-0.98, -0.75)-0.67 (-0.83, -0.52)-0.54 (-0.72, -0.37)-0.65 (-0.81, -0.49)-0.53 (-0.71, -0.36)
p = 0.00p = 0.00p = 0.00p = 0.00p = 0.00
Country: Va.0.62 (0.51, 0.74)0.51 (0.36, 0.65)0.38 (0.19, 0.57)0.49 (0.33, 0.64)0.35 (0.16, 0.55)
p = 0.00p = 0.00p = 0.0001p = 0.00p = 0.0005
Gender: male-0.02 (-0.08, 0.03)0.01 (-0.05, 0.07)
p = 0.42p = 0.72
Age0.05 (-0.02, 0.11)0.04 (-0.02, 0.11)
p = 0.16p = 0.17
Scale: Spiritual Events × Country: Gh.-0.02 (-0.08, 0.05)-0.04 (-0.12, 0.04)-0.06 (-0.14, 0.03)-0.04 (-0.12, 0.05)-0.06 (-0.15, 0.03)
p = 0.66p = 0.37p = 0.19p = 0.37p = 0.18
Scale: Spiritual Events × Country: Th.-0.12 (-0.18, -0.05)-0.09 (-0.16, -0.02)-0.11 (-0.19, -0.03)-0.09 (-0.16, -0.02)-0.12 (-0.19, -0.04)
p = 0.0005p = 0.02p = 0.005p = 0.02p = 0.005
Scale: Spiritual Events × Country: Ch.0.20 (0.13, 0.27)0.16 (0.06, 0.26)0.12 (0.01, 0.24)0.16 (0.06, 0.26)0.12 (0.003, 0.24)
p = 0.00p = 0.002p = 0.04p = 0.002p = 0.05
Scale: Spiritual Events × Country: Va.-0.14 (-0.21, -0.07)-0.12 (-0.21, -0.03)-0.02 (-0.14, 0.11)-0.12 (-0.22, -0.02)-0.01 (-0.14, 0.13)
p = 0.0002p = 0.01p = 0.81p = 0.02p = 0.94
Scale: Spiritual Events × Porosity Vignettes0.03 (-0.01, 0.07)0.03 (-0.01, 0.08)
p = 0.20p = 0.19
Country: Gh. × Porosity Vignettes-0.12 (-0.24, -0.01)-0.13 (-0.25, -0.01)
p = 0.04p = 0.04
Country: Th. × Porosity Vignettes-0.02 (-0.15, 0.11)-0.03 (-0.16, 0.10)
p = 0.78p = 0.62
Country: Ch. × Porosity Vignettes0.08 (-0.08, 0.25)0.07 (-0.10, 0.24)
p = 0.33p = 0.43
Country: Va. × Porosity Vignettes-0.14 (-0.29, 0.02)-0.14 (-0.30, 0.02)
p = 0.08p = 0.09
Scale: Spiritual Events × Porosity Scale-0.03 (-0.07, 0.02)-0.03 (-0.08, 0.02)
p = 0.31p = 0.29
Country: Gh. × Porosity Scale-0.09 (-0.22, 0.03)-0.10 (-0.22, 0.03)
p = 0.15p = 0.14
Country: Th. × Porosity Scale-0.02 (-0.15, 0.11)-0.03 (-0.16, 0.11)
p = 0.75p = 0.68
Country: Ch. × Porosity Scale0.10 (-0.06, 0.26)0.08 (-0.08, 0.25)
p = 0.24p = 0.32
Country: Va. × Porosity Scale-0.15 (-0.32, 0.03)-0.15 (-0.33, 0.03)
p = 0.11p = 0.12
Scale: Spiritual Events:Synesthesia0.04 (0.003, 0.07)0.04 (0.01, 0.07)0.05 (0.01, 0.09)0.06 (0.01, 0.12)0.05 (0.003, 0.09)0.06 (0.01, 0.12)
p = 0.04p = 0.03p = 0.03p = 0.02p = 0.04p = 0.03
Country: Gh. × Synesthesia-0.02 (-0.13, 0.10)0.09 (-0.04, 0.22)0.03 (-0.10, 0.15)0.09 (-0.04, 0.22)0.03 (-0.10, 0.16)
p = 0.76p = 0.19p = 0.68p = 0.20p = 0.67
Country: Th. × Synesthesia-0.09 (-0.20, 0.02)-0.07 (-0.18, 0.04)-0.10 (-0.22, 0.03)-0.06 (-0.18, 0.05)-0.09 (-0.22, 0.03)
p = 0.10p = 0.20p = 0.12p = 0.26p = 0.14
Country: Ch. × Synesthesia-0.06 (-0.18, 0.05)-0.09 (-0.25, 0.06)-0.16 (-0.36, 0.03)-0.09 (-0.25, 0.06)-0.16 (-0.36, 0.03)
p = 0.27p = 0.24p = 0.11p = 0.25p = 0.11
Country: Va. × Synesthesia-0.06 (-0.18, 0.06)-0.06 (-0.20, 0.08)0.09 (-0.12, 0.30)-0.05 (-0.21, 0.10)0.10 (-0.12, 0.31)
p = 0.32p = 0.39p = 0.41p = 0.48p = 0.37
Scale: Spiritual Events × Country: Gh. × Porosity Vignettes0.06 (-0.01, 0.14)0.06 (-0.02, 0.14)
p = 0.10p = 0.12
Scale: Spiritual Events × Country: Th. × Porosity Vignettes-0.0003 (-0.08, 0.08)-0.002 (-0.08, 0.08)
p = 1.00p = 0.97
Scale: Spiritual Events × Country: Ch. × Porosity Vignettes-0.11 (-0.21, 0.001)-0.11 (-0.21, 0.003)
p = 0.06p = 0.06
Scale: Spiritual Events × Country: Va. × Porosity Vignettes-0.02 (-0.11, 0.08)-0.02 (-0.12, 0.08)
p = 0.74p = 0.67
Scale: Spiritual Events × Country: Gh. × Porosity Scale0.12 (0.03, 0.20)0.12 (0.03, 0.20)
p = 0.01p = 0.01
Scale: Spiritual Events × Country: Th. × Porosity Scale0.02 (-0.07, 0.10)0.02 (-0.07, 0.11)
p = 0.74p = 0.73
Scale: Spiritual Events × Country: Ch. × Porosity Scale-0.07 (-0.18, 0.04)-0.06 (-0.18, 0.05)
p = 0.22p = 0.26
Scale: Spiritual Events × Country: Va. × Porosity Scale-0.09 (-0.21, 0.02)-0.10 (-0.22, 0.02)
p = 0.13p = 0.10
Scale: Spiritual Events × Country: Gh. × Synesthesia0.04 (-0.03, 0.10)0.03 (-0.05, 0.12)0.03 (-0.05, 0.12)0.04 (-0.05, 0.12)0.03 (-0.05, 0.12)
p = 0.32p = 0.44p = 0.48p = 0.42p = 0.48
Scale: Spiritual Events × Country: Th. × Synesthesia-0.04 (-0.11, 0.03)-0.06 (-0.13, 0.02)-0.05 (-0.13, 0.03)-0.05 (-0.13, 0.02)-0.05 (-0.13, 0.04)
p = 0.23p = 0.13p = 0.26p = 0.15p = 0.27
Scale: Spiritual Events × Country: Ch. × Synesthesia-0.05 (-0.12, 0.02)-0.04 (-0.14, 0.06)-0.03 (-0.16, 0.10)-0.04 (-0.14, 0.06)-0.03 (-0.16, 0.11)
p = 0.15p = 0.45p = 0.68p = 0.48p = 0.70
Scale: Spiritual Events × Country: Va. × Synesthesia-0.03 (-0.10, 0.04)0.003 (-0.09, 0.09)-0.02 (-0.16, 0.12)-0.003 (-0.10, 0.09)-0.03 (-0.18, 0.11)
p = 0.42p = 0.96p = 0.77p = 0.95p = 0.67
Scale: Spiritual Events × Porosity Vignettes:Synesthesia-0.01 (-0.05, 0.04)-0.005 (-0.05, 0.04)
p = 0.75p = 0.84
Country: Gh. × Porosity Vignettes:Synesthesia-0.14 (-0.26, -0.02)-0.14 (-0.27, -0.02)
p = 0.03p = 0.03
Country: Th. × Porosity Vignettes:Synesthesia0.01 (-0.12, 0.14)-0.002 (-0.14, 0.13)
p = 0.88p = 0.98
Country: Ch. × Porosity Vignettes:Synesthesia-0.07 (-0.24, 0.09)-0.09 (-0.26, 0.08)
p = 0.39p = 0.29
Country: Va. × Porosity Vignettes:Synesthesia0.08 (-0.08, 0.23)0.08 (-0.09, 0.24)
p = 0.32p = 0.38
Scale: Spiritual Events × Country: Gh. × Porosity Vignettes:Synesthesia-0.04 (-0.12, 0.04)-0.04 (-0.12, 0.04)
p = 0.32p = 0.30
Scale: Spiritual Events × Country: Th. × Porosity Vignettes:Synesthesia-0.01 (-0.09, 0.08)-0.01 (-0.10, 0.07)
p = 0.83p = 0.79
Scale: Spiritual Events × Country: Ch. × Porosity Vignettes:Synesthesia0.03 (-0.07, 0.14)0.03 (-0.08, 0.14)
p = 0.57p = 0.60
Scale: Spiritual Events × Country: Va. × Porosity Vignettes:Synesthesia-0.06 (-0.16, 0.03)-0.06 (-0.16, 0.05)
p = 0.21p = 0.30
Scale: Spiritual Events × Porosity Scale:Synesthesia0.01 (-0.04, 0.06)0.01 (-0.04, 0.07)
p = 0.66p = 0.60
Country: Gh. × Porosity Scale:Synesthesia-0.002 (-0.13, 0.12)-0.003 (-0.13, 0.12)
p = 0.99p = 0.97
Country: Th. × Porosity Scale:Synesthesia-0.03 (-0.18, 0.11)-0.03 (-0.17, 0.12)
p = 0.65p = 0.72
Country: Ch. × Porosity Scale:Synesthesia0.02 (-0.16, 0.19)0.01 (-0.17, 0.18)
p = 0.85p = 0.93
Country: Va. × Porosity Scale:Synesthesia-0.08 (-0.27, 0.11)-0.08 (-0.27, 0.11)
p = 0.39p = 0.43
Scale: Spiritual Events × Country: Gh. × Porosity Scale:Synesthesia-0.06 (-0.15, 0.02)-0.07 (-0.15, 0.02)
p = 0.14p = 0.13
Scale: Spiritual Events × Country: Th. × Porosity Scale:Synesthesia0.03 (-0.06, 0.13)0.03 (-0.07, 0.13)
p = 0.51p = 0.55
Scale: Spiritual Events × Country: Ch. × Porosity Scale:Synesthesia0.02 (-0.10, 0.13)0.02 (-0.10, 0.13)
p = 0.77p = 0.79
Scale: Spiritual Events × Country: Va. × Porosity Scale:Synesthesia-0.04 (-0.16, 0.09)-0.03 (-0.16, 0.10)
p = 0.57p = 0.63
Constant-0.001 (-0.08, 0.08)0.02 (-0.04, 0.07)0.06 (-0.004, 0.13)0.07 (-0.003, 0.15)0.05 (-0.01, 0.12)0.08 (0.0002, 0.15)
p = 0.98p = 0.57p = 0.07p = 0.06p = 0.13p = 0.05
Observations1,0081,0081,0081,008978978
Log Likelihood-1,283.45-1,119.01-1,111.12-1,091.12-1,088.93-1,072.40
Akaike Inf. Crit.2,578.912,282.022,306.242,266.242,265.862,232.79
Bayesian Inf. Crit.2,608.402,390.172,512.702,472.702,480.822,447.75
table_s40s41_subscales_MR2 <- stargazer(r105_spirit_MR2, r108_spirit_MR2, 
                                        r109_spirit_MR2, r110_spirit_MR2,
                                        r111_spirit_MR2, r112_spirit_MR2,
                                        type = "html",
                                        ci = T,
                                        report = c("vcsp"),
                                        digits = 2,
                                        digits.extra = 1000,
                                        omit.table.layout = "n",
                                        order = c("^`Aesthetic involvement in nature`$",
                                                  "^`Porosity Vignettes`$",
                                                  "^`Porosity Scale`$",
                                                  "^`Porosity Vignettes`:`Aesthetic involvement in nature`$",
                                                  "^`Porosity Scale`:`Aesthetic involvement in nature`$"),
                                        # dep.var.labels.include = F,
                                        dep.var.caption = "",
                                        single.row = T) 
cat(sub_interact_fun(table_s40s41_subscales_MR2), 
    file = "./stargazer_tables/table_s40s41_subscales_MR2.html")
htmltools::includeHTML("./stargazer_tables/table_s40s41_subscales_MR2.html")
Score
(1)(2)(3)(4)(5)(6)
Aesthetic involvement in nature0.05 (-0.03, 0.13)0.05 (-0.01, 0.11)0.02 (-0.05, 0.09)0.001 (-0.08, 0.08)0.02 (-0.05, 0.09)-0.003 (-0.08, 0.07)
p = 0.19p = 0.09p = 0.62p = 0.98p = 0.65p = 0.95
Porosity Vignettes0.27 (0.20, 0.33)0.27 (0.20, 0.34)
p = 0.00p = 0.00
Porosity Scale0.36 (0.29, 0.43)0.37 (0.30, 0.44)
p = 0.00p = 0.00
Porosity Vignettes:Aesthetic involvement in nature-0.08 (-0.15, -0.005)-0.07 (-0.14, 0.005)
p = 0.04p = 0.07
Porosity Scale:Aesthetic involvement in nature-0.001 (-0.08, 0.08)-0.001 (-0.08, 0.08)
p = 0.98p = 0.99
Scale: Spiritual Events-0.001 (-0.04, 0.03)-0.001 (-0.03, 0.03)-0.02 (-0.06, 0.02)-0.005 (-0.05, 0.04)-0.02 (-0.06, 0.03)-0.002 (-0.05, 0.05)
p = 0.94p = 0.98p = 0.40p = 0.85p = 0.44p = 0.96
Country: Gh.0.83 (0.71, 0.94)0.69 (0.56, 0.81)0.62 (0.49, 0.75)0.66 (0.53, 0.79)0.59 (0.45, 0.72)
p = 0.00p = 0.00p = 0.00p = 0.00p = 0.00
Country: Th.-0.29 (-0.40, -0.17)-0.29 (-0.40, -0.18)-0.25 (-0.36, -0.13)-0.29 (-0.40, -0.17)-0.23 (-0.36, -0.11)
p = 0.000001p = 0.0000005p = 0.00004p = 0.000002p = 0.0002
Country: Ch.-0.86 (-0.98, -0.74)-0.68 (-0.84, -0.53)-0.57 (-0.73, -0.40)-0.66 (-0.82, -0.51)-0.56 (-0.72, -0.39)
p = 0.00p = 0.00p = 0.00p = 0.00p = 0.00
Country: Va.0.62 (0.50, 0.74)0.50 (0.36, 0.65)0.37 (0.17, 0.56)0.48 (0.32, 0.64)0.34 (0.14, 0.54)
p = 0.00p = 0.00p = 0.0003p = 0.00p = 0.001
Gender: male-0.01 (-0.07, 0.05)0.02 (-0.03, 0.08)
p = 0.85p = 0.42
Age0.05 (-0.01, 0.12)0.04 (-0.02, 0.11)
p = 0.12p = 0.18
Scale: Spiritual Events × Country: Gh.-0.02 (-0.08, 0.05)-0.06 (-0.14, 0.02)-0.07 (-0.16, 0.01)-0.06 (-0.15, 0.02)-0.07 (-0.16, 0.01)
p = 0.66p = 0.13p = 0.11p = 0.13p = 0.10
Scale: Spiritual Events × Country: Th.-0.12 (-0.18, -0.05)-0.09 (-0.16, -0.02)-0.12 (-0.20, -0.04)-0.09 (-0.17, -0.02)-0.13 (-0.20, -0.05)
p = 0.001p = 0.02p = 0.003p = 0.02p = 0.002
Scale: Spiritual Events × Country: Ch.0.20 (0.13, 0.27)0.16 (0.06, 0.26)0.13 (0.02, 0.23)0.16 (0.06, 0.26)0.12 (0.01, 0.23)
p = 0.00p = 0.002p = 0.03p = 0.002p = 0.03
Scale: Spiritual Events × Country: Va.-0.14 (-0.21, -0.07)-0.12 (-0.21, -0.03)-0.02 (-0.15, 0.11)-0.12 (-0.22, -0.02)-0.01 (-0.14, 0.12)
p = 0.0002p = 0.01p = 0.77p = 0.02p = 0.90
Scale: Spiritual Events × Porosity Vignettes0.04 (-0.004, 0.08)0.04 (-0.003, 0.09)
p = 0.08p = 0.07
Country: Gh. × Porosity Vignettes-0.11 (-0.23, 0.004)-0.12 (-0.23, 0.001)
p = 0.06p = 0.06
Country: Th. × Porosity Vignettes-0.05 (-0.17, 0.08)-0.06 (-0.18, 0.07)
p = 0.47p = 0.37
Country: Ch. × Porosity Vignettes0.06 (-0.11, 0.23)0.05 (-0.12, 0.22)
p = 0.47p = 0.57
Country: Va. × Porosity Vignettes-0.15 (-0.30, 0.001)-0.15 (-0.31, 0.01)
p = 0.06p = 0.07
Scale: Spiritual Events × Porosity Scale-0.01 (-0.06, 0.03)-0.02 (-0.06, 0.03)
p = 0.58p = 0.55
Country: Gh. × Porosity Scale-0.09 (-0.22, 0.04)-0.09 (-0.22, 0.03)
p = 0.16p = 0.15
Country: Th. × Porosity Scale-0.05 (-0.18, 0.09)-0.05 (-0.19, 0.08)
p = 0.49p = 0.44
Country: Ch. × Porosity Scale0.07 (-0.09, 0.22)0.06 (-0.10, 0.21)
p = 0.41p = 0.49
Country: Va. × Porosity Scale-0.15 (-0.33, 0.03)-0.15 (-0.33, 0.04)
p = 0.10p = 0.12
Scale: Spiritual Events:Aesthetic involvement in nature0.02 (-0.02, 0.06)0.02 (-0.01, 0.05)0.03 (-0.01, 0.08)0.04 (-0.01, 0.09)0.03 (-0.01, 0.08)0.03 (-0.02, 0.09)
p = 0.29p = 0.25p = 0.15p = 0.16p = 0.16p = 0.19
Country: Gh. × Aesthetic involvement in nature-0.03 (-0.15, 0.09)0.03 (-0.09, 0.16)0.03 (-0.10, 0.16)0.03 (-0.09, 0.16)0.03 (-0.10, 0.16)
p = 0.62p = 0.61p = 0.69p = 0.64p = 0.68
Country: Th. × Aesthetic involvement in nature0.10 (-0.01, 0.22)0.12 (0.01, 0.23)0.11 (-0.01, 0.23)0.12 (0.01, 0.24)0.12 (0.01, 0.24)
p = 0.08p = 0.05p = 0.07p = 0.04p = 0.05
Country: Ch. × Aesthetic involvement in nature-0.03 (-0.15, 0.09)-0.05 (-0.22, 0.12)-0.02 (-0.20, 0.15)-0.05 (-0.22, 0.12)-0.02 (-0.20, 0.15)
p = 0.63p = 0.56p = 0.82p = 0.56p = 0.80
Country: Va. × Aesthetic involvement in nature-0.08 (-0.20, 0.04)-0.01 (-0.17, 0.15)-0.11 (-0.31, 0.09)-0.02 (-0.19, 0.14)-0.13 (-0.33, 0.08)
p = 0.21p = 0.90p = 0.29p = 0.79p = 0.23
Scale: Spiritual Events × Country: Gh. × Porosity Vignettes0.07 (-0.003, 0.14)0.07 (-0.01, 0.14)
p = 0.07p = 0.08
Scale: Spiritual Events × Country: Th. × Porosity Vignettes-0.01 (-0.09, 0.07)-0.01 (-0.09, 0.07)
p = 0.83p = 0.79
Scale: Spiritual Events × Country: Ch. × Porosity Vignettes-0.11 (-0.22, -0.01)-0.11 (-0.22, -0.01)
p = 0.04p = 0.04
Scale: Spiritual Events × Country: Va. × Porosity Vignettes-0.03 (-0.13, 0.07)-0.03 (-0.13, 0.07)
p = 0.55p = 0.56
Scale: Spiritual Events × Country: Gh. × Porosity Scale0.12 (0.04, 0.20)0.12 (0.04, 0.21)
p = 0.01p = 0.01
Scale: Spiritual Events × Country: Th. × Porosity Scale0.01 (-0.08, 0.10)0.01 (-0.08, 0.10)
p = 0.87p = 0.86
Scale: Spiritual Events × Country: Ch. × Porosity Scale-0.08 (-0.18, 0.03)-0.07 (-0.18, 0.03)
p = 0.15p = 0.17
Scale: Spiritual Events × Country: Va. × Porosity Scale-0.11 (-0.22, 0.01)-0.12 (-0.24, 0.01)
p = 0.08p = 0.07
Scale: Spiritual Events × Country: Gh. × Aesthetic involvement in nature0.03 (-0.04, 0.10)0.02 (-0.06, 0.10)0.03 (-0.05, 0.12)0.02 (-0.06, 0.10)0.03 (-0.05, 0.12)
p = 0.39p = 0.57p = 0.48p = 0.57p = 0.45
Scale: Spiritual Events × Country: Th. × Aesthetic involvement in nature-0.03 (-0.10, 0.03)-0.04 (-0.11, 0.03)-0.04 (-0.11, 0.04)-0.04 (-0.11, 0.03)-0.03 (-0.11, 0.05)
p = 0.32p = 0.26p = 0.38p = 0.28p = 0.43
Scale: Spiritual Events × Country: Ch. × Aesthetic involvement in nature0.003 (-0.07, 0.07)0.004 (-0.10, 0.11)0.03 (-0.09, 0.14)0.004 (-0.10, 0.11)0.03 (-0.09, 0.14)
p = 0.95p = 0.95p = 0.67p = 0.95p = 0.66
Scale: Spiritual Events × Country: Va. × Aesthetic involvement in nature-0.03 (-0.10, 0.05)0.02 (-0.08, 0.12)-0.03 (-0.16, 0.10)0.02 (-0.09, 0.13)-0.04 (-0.17, 0.10)
p = 0.49p = 0.70p = 0.67p = 0.76p = 0.57
Scale: Spiritual Events × Porosity Vignettes:Aesthetic involvement in nature-0.004 (-0.05, 0.04)-0.004 (-0.05, 0.04)
p = 0.86p = 0.87
Country: Gh. × Porosity Vignettes:Aesthetic involvement in nature0.01 (-0.11, 0.13)0.004 (-0.12, 0.12)
p = 0.90p = 0.96
Country: Th. × Porosity Vignettes:Aesthetic involvement in nature-0.001 (-0.13, 0.13)-0.01 (-0.14, 0.12)
p = 1.00p = 0.88
Country: Ch. × Porosity Vignettes:Aesthetic involvement in nature-0.01 (-0.20, 0.17)-0.02 (-0.20, 0.16)
p = 0.88p = 0.83
Country: Va. × Porosity Vignettes:Aesthetic involvement in nature0.02 (-0.14, 0.19)0.02 (-0.15, 0.18)
p = 0.77p = 0.86
Scale: Spiritual Events × Country: Gh. × Porosity Vignettes:Aesthetic involvement in nature-0.01 (-0.09, 0.06)-0.01 (-0.09, 0.07)
p = 0.76p = 0.77
Scale: Spiritual Events × Country: Th. × Porosity Vignettes:Aesthetic involvement in nature0.05 (-0.04, 0.13)0.05 (-0.04, 0.13)
p = 0.28p = 0.29
Scale: Spiritual Events × Country: Ch. × Porosity Vignettes:Aesthetic involvement in nature0.03 (-0.09, 0.14)0.03 (-0.09, 0.14)
p = 0.66p = 0.67
Scale: Spiritual Events × Country: Va. × Porosity Vignettes:Aesthetic involvement in nature-0.08 (-0.18, 0.03)-0.08 (-0.18, 0.03)
p = 0.15p = 0.17
Scale: Spiritual Events × Porosity Scale:Aesthetic involvement in nature0.02 (-0.03, 0.07)0.02 (-0.03, 0.07)
p = 0.54p = 0.49
Country: Gh. × Porosity Scale:Aesthetic involvement in nature0.01 (-0.12, 0.14)0.01 (-0.12, 0.14)
p = 0.84p = 0.89
Country: Th. × Porosity Scale:Aesthetic involvement in nature-0.02 (-0.16, 0.12)-0.02 (-0.17, 0.12)
p = 0.78p = 0.75
Country: Ch. × Porosity Scale:Aesthetic involvement in nature-0.01 (-0.18, 0.15)-0.02 (-0.19, 0.14)
p = 0.87p = 0.78
Country: Va. × Porosity Scale:Aesthetic involvement in nature0.09 (-0.11, 0.29)0.10 (-0.11, 0.30)
p = 0.39p = 0.36
Scale: Spiritual Events × Country: Gh. × Porosity Scale:Aesthetic involvement in nature-0.04 (-0.12, 0.05)-0.04 (-0.13, 0.05)
p = 0.39p = 0.37
Scale: Spiritual Events × Country: Th. × Porosity Scale:Aesthetic involvement in nature0.05 (-0.04, 0.15)0.05 (-0.05, 0.14)
p = 0.27p = 0.31
Scale: Spiritual Events × Country: Ch. × Porosity Scale:Aesthetic involvement in nature0.02 (-0.09, 0.13)0.02 (-0.09, 0.13)
p = 0.71p = 0.76
Scale: Spiritual Events × Country: Va. × Porosity Scale:Aesthetic involvement in nature-0.03 (-0.16, 0.11)-0.02 (-0.15, 0.12)
p = 0.71p = 0.83
Constant-0.001 (-0.08, 0.08)0.02 (-0.04, 0.07)0.07 (0.002, 0.13)0.07 (-0.003, 0.14)0.06 (-0.004, 0.13)0.08 (0.0005, 0.15)
p = 0.98p = 0.58p = 0.05p = 0.07p = 0.07p = 0.05
Observations1,0081,0081,0081,008978978
Log Likelihood-1,289.22-1,138.31-1,119.25-1,105.08-1,097.45-1,084.56
Akaike Inf. Crit.2,590.442,320.622,322.502,294.162,282.892,257.11
Bayesian Inf. Crit.2,619.932,428.772,528.962,500.622,497.862,472.08
table_s40s41_subscales_MR3 <- stargazer(r105_spirit_MR3, r108_spirit_MR3, 
                                        r109_spirit_MR3, r110_spirit_MR3,
                                        r111_spirit_MR3, r112_spirit_MR3,
                                        type = "html",
                                        ci = T,
                                        report = c("vcsp"),
                                        digits = 2,
                                        digits.extra = 1000,
                                        omit.table.layout = "n",
                                        order = c("^`Altered states of consciousness \\(a\\)`$",
                                                  "^`Porosity Vignettes`$",
                                                  "^`Porosity Scale`$",
                                                  "^`Porosity Vignettes`:`Altered states of consciousness \\(a\\)`$",
                                                  "^`Porosity Scale`:`Altered states of consciousness \\(a\\)`$"),
                                        # dep.var.labels.include = F,
                                        dep.var.caption = "",
                                        single.row = T) 
cat(sub_interact_fun(table_s40s41_subscales_MR3), 
    file = "./stargazer_tables/table_s40s41_subscales_MR3.html")
htmltools::includeHTML("./stargazer_tables/table_s40s41_subscales_MR3.html")
Score
(1)(2)(3)(4)(5)(6)
Altered states of consciousness (a)0.11 (0.03, 0.19)0.10 (0.04, 0.16)0.07 (0.001, 0.15)0.07 (-0.01, 0.14)0.07 (-0.002, 0.15)0.07 (-0.01, 0.14)
p = 0.01p = 0.001p = 0.05p = 0.09p = 0.06p = 0.10
Porosity Vignettes0.25 (0.18, 0.32)0.25 (0.18, 0.32)
p = 0.00p = 0.00
Porosity Scale0.35 (0.28, 0.42)0.36 (0.29, 0.43)
p = 0.00p = 0.00
Porosity Vignettes:Altered states of consciousness (a)-0.04 (-0.12, 0.03)-0.03 (-0.11, 0.04)
p = 0.27p = 0.39
Porosity Scale:Altered states of consciousness (a)0.01 (-0.06, 0.09)0.02 (-0.06, 0.09)
p = 0.71p = 0.63
Scale: Spiritual Events-0.002 (-0.04, 0.03)-0.001 (-0.04, 0.03)-0.01 (-0.06, 0.03)-0.01 (-0.06, 0.04)-0.01 (-0.05, 0.03)-0.004 (-0.05, 0.05)
p = 0.94p = 0.97p = 0.59p = 0.78p = 0.66p = 0.89
Country: Gh.0.83 (0.71, 0.94)0.70 (0.57, 0.83)0.63 (0.50, 0.76)0.68 (0.54, 0.81)0.60 (0.46, 0.74)
p = 0.00p = 0.00p = 0.00p = 0.00p = 0.00
Country: Th.-0.29 (-0.40, -0.17)-0.32 (-0.44, -0.20)-0.26 (-0.37, -0.14)-0.32 (-0.45, -0.20)-0.24 (-0.37, -0.12)
p = 0.000001p = 0.0000002p = 0.00002p = 0.0000005p = 0.0002
Country: Ch.-0.86 (-0.98, -0.75)-0.68 (-0.84, -0.52)-0.58 (-0.74, -0.42)-0.66 (-0.82, -0.50)-0.57 (-0.74, -0.41)
p = 0.00p = 0.00p = 0.00p = 0.00p = 0.00
Country: Va.0.62 (0.50, 0.74)0.51 (0.36, 0.66)0.39 (0.19, 0.58)0.49 (0.33, 0.66)0.36 (0.15, 0.56)
p = 0.00p = 0.00p = 0.0002p = 0.00p = 0.001
Gender: male-0.01 (-0.07, 0.04)0.02 (-0.03, 0.08)
p = 0.63p = 0.41
Age0.05 (-0.02, 0.11)0.04 (-0.02, 0.11)
p = 0.16p = 0.21
Scale: Spiritual Events × Country: Gh.-0.02 (-0.08, 0.05)-0.06 (-0.14, 0.02)-0.07 (-0.16, 0.01)-0.06 (-0.15, 0.02)-0.08 (-0.17, 0.01)
p = 0.65p = 0.15p = 0.11p = 0.14p = 0.10
Scale: Spiritual Events × Country: Th.-0.12 (-0.18, -0.05)-0.09 (-0.17, -0.02)-0.12 (-0.20, -0.04)-0.10 (-0.17, -0.02)-0.12 (-0.20, -0.04)
p = 0.001p = 0.02p = 0.004p = 0.02p = 0.003
Scale: Spiritual Events × Country: Ch.0.20 (0.13, 0.27)0.16 (0.06, 0.26)0.12 (0.01, 0.23)0.16 (0.05, 0.26)0.12 (0.01, 0.23)
p = 0.00000002p = 0.002p = 0.04p = 0.003p = 0.04
Scale: Spiritual Events × Country: Va.-0.14 (-0.21, -0.07)-0.11 (-0.21, -0.02)-0.02 (-0.15, 0.11)-0.11 (-0.21, -0.01)-0.01 (-0.15, 0.13)
p = 0.0002p = 0.03p = 0.75p = 0.04p = 0.90
Scale: Spiritual Events × Porosity Vignettes0.04 (-0.003, 0.09)0.04 (-0.003, 0.09)
p = 0.07p = 0.07
Country: Gh. × Porosity Vignettes-0.09 (-0.21, 0.02)-0.10 (-0.22, 0.02)
p = 0.12p = 0.11
Country: Th. × Porosity Vignettes-0.09 (-0.22, 0.05)-0.10 (-0.24, 0.04)
p = 0.21p = 0.16
Country: Ch. × Porosity Vignettes0.08 (-0.09, 0.25)0.07 (-0.10, 0.24)
p = 0.34p = 0.42
Country: Va. × Porosity Vignettes-0.15 (-0.31, 0.01)-0.15 (-0.31, 0.01)
p = 0.07p = 0.08
Scale: Spiritual Events × Porosity Scale-0.02 (-0.07, 0.03)-0.02 (-0.07, 0.03)
p = 0.49p = 0.44
Country: Gh. × Porosity Scale-0.08 (-0.21, 0.04)-0.09 (-0.22, 0.04)
p = 0.20p = 0.18
Country: Th. × Porosity Scale-0.04 (-0.18, 0.09)-0.05 (-0.18, 0.09)
p = 0.52p = 0.48
Country: Ch. × Porosity Scale0.06 (-0.09, 0.22)0.05 (-0.11, 0.21)
p = 0.44p = 0.54
Country: Va. × Porosity Scale-0.16 (-0.34, 0.02)-0.15 (-0.33, 0.03)
p = 0.09p = 0.11
Scale: Spiritual Events:Altered states of consciousness (a)0.01 (-0.03, 0.05)0.01 (-0.02, 0.04)0.02 (-0.03, 0.06)0.01 (-0.04, 0.06)0.02 (-0.03, 0.06)0.01 (-0.04, 0.06)
p = 0.58p = 0.59p = 0.49p = 0.67p = 0.52p = 0.67
Country: Gh. × Altered states of consciousness (a)-0.05 (-0.17, 0.07)0.04 (-0.09, 0.18)-0.03 (-0.16, 0.11)0.04 (-0.09, 0.18)-0.02 (-0.16, 0.11)
p = 0.39p = 0.55p = 0.69p = 0.54p = 0.75
Country: Th. × Altered states of consciousness (a)0.07 (-0.04, 0.19)0.10 (-0.04, 0.23)0.11 (-0.02, 0.23)0.10 (-0.04, 0.24)0.11 (-0.01, 0.23)
p = 0.19p = 0.17p = 0.10p = 0.16p = 0.08
Country: Ch. × Altered states of consciousness (a)-0.03 (-0.15, 0.09)-0.03 (-0.21, 0.16)0.02 (-0.17, 0.20)-0.03 (-0.22, 0.16)0.02 (-0.17, 0.21)
p = 0.63p = 0.79p = 0.88p = 0.74p = 0.83
Country: Va. × Altered states of consciousness (a)-0.04 (-0.16, 0.08)-0.02 (-0.16, 0.13)-0.07 (-0.25, 0.12)-0.02 (-0.17, 0.12)-0.07 (-0.26, 0.11)
p = 0.56p = 0.84p = 0.49p = 0.76p = 0.45
Scale: Spiritual Events × Country: Gh. × Porosity Vignettes0.07 (-0.003, 0.15)0.07 (-0.01, 0.15)
p = 0.06p = 0.07
Scale: Spiritual Events × Country: Th. × Porosity Vignettes-0.002 (-0.09, 0.08)-0.003 (-0.09, 0.08)
p = 0.97p = 0.95
Scale: Spiritual Events × Country: Ch. × Porosity Vignettes-0.11 (-0.22, -0.005)-0.11 (-0.22, -0.003)
p = 0.05p = 0.05
Scale: Spiritual Events × Country: Va. × Porosity Vignettes-0.04 (-0.14, 0.06)-0.05 (-0.15, 0.06)
p = 0.41p = 0.36
Scale: Spiritual Events × Country: Gh. × Porosity Scale0.12 (0.03, 0.20)0.12 (0.03, 0.21)
p = 0.01p = 0.01
Scale: Spiritual Events × Country: Th. × Porosity Scale-0.001 (-0.09, 0.09)0.00002 (-0.09, 0.09)
p = 0.99p = 1.00
Scale: Spiritual Events × Country: Ch. × Porosity Scale-0.08 (-0.18, 0.02)-0.08 (-0.18, 0.03)
p = 0.13p = 0.16
Scale: Spiritual Events × Country: Va. × Porosity Scale-0.10 (-0.22, 0.02)-0.11 (-0.23, 0.01)
p = 0.11p = 0.09
Scale: Spiritual Events × Country: Gh. × Altered states of consciousness (a)0.004 (-0.07, 0.07)0.05 (-0.04, 0.13)-0.04 (-0.13, 0.05)0.05 (-0.04, 0.13)-0.04 (-0.13, 0.06)
p = 0.93p = 0.30p = 0.44p = 0.30p = 0.45
Scale: Spiritual Events × Country: Th. × Altered states of consciousness (a)-0.01 (-0.08, 0.06)-0.03 (-0.12, 0.05)0.003 (-0.08, 0.09)-0.03 (-0.12, 0.06)0.003 (-0.08, 0.09)
p = 0.75p = 0.47p = 0.94p = 0.48p = 0.96
Scale: Spiritual Events × Country: Ch. × Altered states of consciousness (a)0.02 (-0.05, 0.09)-0.02 (-0.14, 0.10)0.06 (-0.06, 0.19)-0.02 (-0.14, 0.10)0.06 (-0.06, 0.19)
p = 0.62p = 0.72p = 0.33p = 0.73p = 0.34
Scale: Spiritual Events × Country: Va. × Altered states of consciousness (a)-0.02 (-0.09, 0.05)0.02 (-0.07, 0.11)-0.03 (-0.15, 0.10)0.01 (-0.08, 0.11)-0.02 (-0.15, 0.10)
p = 0.56p = 0.73p = 0.68p = 0.78p = 0.71
Scale: Spiritual Events × Porosity Vignettes:Altered states of consciousness (a)-0.04 (-0.09, 0.01)-0.04 (-0.09, 0.01)
p = 0.09p = 0.11
Country: Gh. × Porosity Vignettes:Altered states of consciousness (a)-0.08 (-0.21, 0.04)-0.08 (-0.20, 0.05)
p = 0.19p = 0.22
Country: Th. × Porosity Vignettes:Altered states of consciousness (a)0.11 (-0.05, 0.27)0.11 (-0.06, 0.27)
p = 0.18p = 0.21
Country: Ch. × Porosity Vignettes:Altered states of consciousness (a)0.03 (-0.17, 0.22)0.003 (-0.20, 0.20)
p = 0.80p = 0.98
Country: Va. × Porosity Vignettes:Altered states of consciousness (a)0.03 (-0.12, 0.18)0.03 (-0.12, 0.19)
p = 0.72p = 0.68
Scale: Spiritual Events × Country: Gh. × Porosity Vignettes:Altered states of consciousness (a)-0.05 (-0.13, 0.03)-0.05 (-0.13, 0.03)
p = 0.19p = 0.19
Scale: Spiritual Events × Country: Th. × Porosity Vignettes:Altered states of consciousness (a)0.03 (-0.07, 0.13)0.03 (-0.07, 0.13)
p = 0.57p = 0.59
Scale: Spiritual Events × Country: Ch. × Porosity Vignettes:Altered states of consciousness (a)-0.002 (-0.13, 0.12)-0.003 (-0.13, 0.12)
p = 0.98p = 0.97
Scale: Spiritual Events × Country: Va. × Porosity Vignettes:Altered states of consciousness (a)-0.04 (-0.14, 0.05)-0.04 (-0.14, 0.06)
p = 0.37p = 0.43
Scale: Spiritual Events × Porosity Scale:Altered states of consciousness (a)0.02 (-0.03, 0.07)0.02 (-0.03, 0.07)
p = 0.38p = 0.39
Country: Gh. × Porosity Scale:Altered states of consciousness (a)-0.07 (-0.21, 0.08)-0.06 (-0.20, 0.09)
p = 0.37p = 0.44
Country: Th. × Porosity Scale:Altered states of consciousness (a)0.06 (-0.08, 0.20)0.06 (-0.08, 0.20)
p = 0.38p = 0.41
Country: Ch. × Porosity Scale:Altered states of consciousness (a)0.04 (-0.13, 0.21)0.04 (-0.14, 0.21)
p = 0.64p = 0.68
Country: Va. × Porosity Scale:Altered states of consciousness (a)0.04 (-0.12, 0.20)0.04 (-0.12, 0.20)
p = 0.63p = 0.64
Scale: Spiritual Events × Country: Gh. × Porosity Scale:Altered states of consciousness (a)0.02 (-0.08, 0.12)0.02 (-0.08, 0.12)
p = 0.68p = 0.69
Scale: Spiritual Events × Country: Th. × Porosity Scale:Altered states of consciousness (a)0.01 (-0.08, 0.11)0.02 (-0.08, 0.11)
p = 0.76p = 0.72
Scale: Spiritual Events × Country: Ch. × Porosity Scale:Altered states of consciousness (a)0.02 (-0.10, 0.13)0.02 (-0.10, 0.14)
p = 0.78p = 0.77
Scale: Spiritual Events × Country: Va. × Porosity Scale:Altered states of consciousness (a)-0.01 (-0.12, 0.10)-0.01 (-0.12, 0.10)
p = 0.85p = 0.81
Constant-0.002 (-0.08, 0.08)0.02 (-0.04, 0.07)0.07 (0.001, 0.14)0.07 (-0.01, 0.14)0.06 (-0.01, 0.13)0.07 (-0.003, 0.15)
p = 0.97p = 0.58p = 0.05p = 0.08p = 0.08p = 0.07
Observations1,0081,0081,0081,008978978
Log Likelihood-1,287.18-1,135.96-1,114.60-1,104.26-1,093.86-1,084.20
Akaike Inf. Crit.2,586.372,315.932,313.202,292.532,275.722,256.40
Bayesian Inf. Crit.2,615.862,424.072,519.662,498.992,490.682,471.37
table_s40s41_subscales_MR4 <- stargazer(r105_spirit_MR4, r108_spirit_MR4, 
                                        r109_spirit_MR4, r110_spirit_MR4,
                                        r111_spirit_MR4, r112_spirit_MR4,
                                        type = "html",
                                        ci = T,
                                        report = c("vcsp"),
                                        digits = 2,
                                        digits.extra = 1000,
                                        omit.table.layout = "n",
                                        order = c("^`Imaginative involvement`$",
                                                  "^`Porosity Vignettes`$",
                                                  "^`Porosity Scale`$",
                                                  "^`Porosity Vignettes`:`Imaginative involvement`$",
                                                  "^`Porosity Scale`:`Imaginative involvement`$"),
                                        # dep.var.labels.include = F,
                                        dep.var.caption = "",
                                        single.row = T) 
cat(sub_interact_fun(table_s40s41_subscales_MR4), 
    file = "./stargazer_tables/table_s40s41_subscales_MR4.html")
htmltools::includeHTML("./stargazer_tables/table_s40s41_subscales_MR4.html")
Score
(1)(2)(3)(4)(5)(6)
Imaginative involvement0.09 (0.01, 0.17)0.09 (0.03, 0.15)0.08 (0.01, 0.14)0.05 (-0.02, 0.12)0.08 (0.01, 0.14)0.05 (-0.03, 0.13)
p = 0.03p = 0.004p = 0.02p = 0.19p = 0.03p = 0.23
Porosity Vignettes0.26 (0.19, 0.33)0.26 (0.19, 0.33)
p = 0.00p = 0.00
Porosity Scale0.36 (0.28, 0.43)0.36 (0.29, 0.44)
p = 0.00p = 0.00
Porosity Vignettes:Imaginative involvement0.01 (-0.06, 0.08)0.01 (-0.06, 0.09)
p = 0.81p = 0.75
Porosity Scale:Imaginative involvement0.06 (-0.02, 0.13)0.06 (-0.01, 0.14)
p = 0.13p = 0.10
Scale: Spiritual Events-0.002 (-0.04, 0.03)-0.0005 (-0.03, 0.03)-0.02 (-0.06, 0.02)-0.01 (-0.05, 0.04)-0.02 (-0.06, 0.02)-0.003 (-0.05, 0.05)
p = 0.94p = 0.98p = 0.35p = 0.84p = 0.37p = 0.91
Country: Gh.0.83 (0.71, 0.94)0.71 (0.58, 0.84)0.64 (0.51, 0.77)0.69 (0.56, 0.82)0.62 (0.48, 0.75)
p = 0.00p = 0.00p = 0.00p = 0.00p = 0.00
Country: Th.-0.29 (-0.40, -0.17)-0.30 (-0.41, -0.19)-0.25 (-0.37, -0.13)-0.30 (-0.42, -0.19)-0.24 (-0.36, -0.12)
p = 0.000001p = 0.0000002p = 0.00003p = 0.0000003p = 0.0002
Country: Ch.-0.86 (-0.98, -0.74)-0.70 (-0.86, -0.54)-0.60 (-0.76, -0.43)-0.68 (-0.84, -0.52)-0.59 (-0.75, -0.42)
p = 0.00p = 0.00p = 0.00p = 0.00p = 0.00
Country: Va.0.62 (0.50, 0.74)0.51 (0.36, 0.65)0.39 (0.19, 0.58)0.48 (0.32, 0.63)0.36 (0.16, 0.56)
p = 0.00p = 0.00p = 0.0001p = 0.00p = 0.0005
Gender: male-0.02 (-0.08, 0.04)0.02 (-0.04, 0.08)
p = 0.55p = 0.51
Age0.05 (-0.02, 0.12)0.03 (-0.03, 0.10)
p = 0.14p = 0.31
Scale: Spiritual Events × Country: Gh.-0.02 (-0.08, 0.05)-0.06 (-0.14, 0.02)-0.07 (-0.16, 0.01)-0.06 (-0.14, 0.03)-0.08 (-0.16, 0.01)
p = 0.66p = 0.18p = 0.10p = 0.18p = 0.10
Scale: Spiritual Events × Country: Th.-0.12 (-0.18, -0.05)-0.09 (-0.16, -0.02)-0.11 (-0.19, -0.04)-0.09 (-0.16, -0.02)-0.12 (-0.20, -0.04)
p = 0.001p = 0.01p = 0.004p = 0.02p = 0.004
Scale: Spiritual Events × Country: Ch.0.20 (0.13, 0.27)0.15 (0.05, 0.25)0.11 (0.01, 0.22)0.15 (0.05, 0.25)0.11 (0.001, 0.22)
p = 0.00p = 0.003p = 0.04p = 0.003p = 0.05
Scale: Spiritual Events × Country: Va.-0.14 (-0.21, -0.07)-0.12 (-0.21, -0.03)-0.01 (-0.14, 0.12)-0.12 (-0.21, -0.02)-0.01 (-0.14, 0.12)
p = 0.0002p = 0.02p = 0.85p = 0.02p = 0.92
Scale: Spiritual Events × Porosity Vignettes0.04 (0.00002, 0.09)0.05 (0.002, 0.09)
p = 0.05p = 0.05
Country: Gh. × Porosity Vignettes-0.11 (-0.23, 0.01)-0.12 (-0.23, 0.004)
p = 0.07p = 0.06
Country: Th. × Porosity Vignettes-0.06 (-0.18, 0.07)-0.06 (-0.19, 0.06)
p = 0.39p = 0.32
Country: Ch. × Porosity Vignettes0.06 (-0.11, 0.22)0.04 (-0.13, 0.21)
p = 0.52p = 0.63
Country: Va. × Porosity Vignettes-0.15 (-0.30, -0.001)-0.15 (-0.31, 0.01)
p = 0.05p = 0.07
Scale: Spiritual Events × Porosity Scale-0.01 (-0.06, 0.04)-0.01 (-0.06, 0.04)
p = 0.68p = 0.64
Country: Gh. × Porosity Scale-0.08 (-0.21, 0.05)-0.09 (-0.22, 0.04)
p = 0.21p = 0.18
Country: Th. × Porosity Scale-0.02 (-0.15, 0.11)-0.03 (-0.16, 0.11)
p = 0.74p = 0.70
Country: Ch. × Porosity Scale0.04 (-0.11, 0.20)0.03 (-0.12, 0.19)
p = 0.59p = 0.68
Country: Va. × Porosity Scale-0.17 (-0.35, 0.01)-0.16 (-0.34, 0.02)
p = 0.07p = 0.08
Scale: Spiritual Events:Imaginative involvement0.004 (-0.03, 0.04)0.004 (-0.03, 0.04)0.01 (-0.03, 0.05)0.004 (-0.05, 0.05)0.01 (-0.04, 0.05)0.002 (-0.05, 0.05)
p = 0.84p = 0.83p = 0.77p = 0.89p = 0.80p = 0.95
Country: Gh. × Imaginative involvement-0.01 (-0.12, 0.11)-0.01 (-0.13, 0.11)-0.03 (-0.15, 0.09)-0.001 (-0.12, 0.12)-0.02 (-0.14, 0.11)
p = 0.91p = 0.85p = 0.67p = 0.99p = 0.78
Country: Th. × Imaginative involvement0.07 (-0.04, 0.19)0.09 (-0.02, 0.20)0.11 (-0.001, 0.23)0.09 (-0.02, 0.20)0.11 (-0.003, 0.23)
p = 0.20p = 0.13p = 0.06p = 0.13p = 0.06
Country: Ch. × Imaginative involvement0.02 (-0.10, 0.13)0.11 (-0.06, 0.27)0.12 (-0.06, 0.29)0.11 (-0.05, 0.28)0.12 (-0.06, 0.30)
p = 0.78p = 0.20p = 0.19p = 0.19p = 0.19
Country: Va. × Imaginative involvement-0.02 (-0.14, 0.10)-0.01 (-0.14, 0.12)-0.14 (-0.33, 0.06)-0.02 (-0.17, 0.12)-0.17 (-0.38, 0.04)
p = 0.79p = 0.86p = 0.17p = 0.75p = 0.13
Scale: Spiritual Events × Country: Gh. × Porosity Vignettes0.10 (0.03, 0.18)0.10 (0.02, 0.18)
p = 0.01p = 0.01
Scale: Spiritual Events × Country: Th. × Porosity Vignettes-0.003 (-0.08, 0.07)-0.01 (-0.09, 0.07)
p = 0.95p = 0.88
Scale: Spiritual Events × Country: Ch. × Porosity Vignettes-0.14 (-0.24, -0.03)-0.14 (-0.25, -0.03)
p = 0.02p = 0.02
Scale: Spiritual Events × Country: Va. × Porosity Vignettes-0.04 (-0.14, 0.05)-0.04 (-0.14, 0.06)
p = 0.40p = 0.42
Scale: Spiritual Events × Country: Gh. × Porosity Scale0.16 (0.07, 0.24)0.16 (0.07, 0.25)
p = 0.0004p = 0.0004
Scale: Spiritual Events × Country: Th. × Porosity Scale0.004 (-0.08, 0.09)0.003 (-0.09, 0.09)
p = 0.94p = 0.95
Scale: Spiritual Events × Country: Ch. × Porosity Scale-0.10 (-0.20, 0.01)-0.10 (-0.20, 0.01)
p = 0.07p = 0.08
Scale: Spiritual Events × Country: Va. × Porosity Scale-0.12 (-0.24, -0.003)-0.13 (-0.25, -0.01)
p = 0.05p = 0.04
Scale: Spiritual Events × Country: Gh. × Imaginative involvement-0.08 (-0.14, -0.01)-0.08 (-0.16, -0.01)-0.10 (-0.18, -0.02)-0.08 (-0.16, -0.01)-0.10 (-0.18, -0.02)
p = 0.04p = 0.03p = 0.02p = 0.04p = 0.02
Scale: Spiritual Events × Country: Th. × Imaginative involvement-0.06 (-0.12, 0.01)-0.05 (-0.12, 0.02)-0.05 (-0.13, 0.03)-0.05 (-0.12, 0.02)-0.05 (-0.13, 0.03)
p = 0.09p = 0.16p = 0.21p = 0.18p = 0.24
Scale: Spiritual Events × Country: Ch. × Imaginative involvement0.07 (0.01, 0.14)0.06 (-0.04, 0.16)0.12 (0.004, 0.24)0.06 (-0.04, 0.17)0.12 (0.004, 0.24)
p = 0.04p = 0.24p = 0.05p = 0.25p = 0.05
Scale: Spiritual Events × Country: Va. × Imaginative involvement0.04 (-0.03, 0.11)0.06 (-0.02, 0.14)0.02 (-0.11, 0.15)0.05 (-0.04, 0.14)0.01 (-0.13, 0.15)
p = 0.24p = 0.16p = 0.81p = 0.29p = 0.93
Scale: Spiritual Events × Porosity Vignettes:Imaginative involvement-0.02 (-0.06, 0.03)-0.02 (-0.06, 0.03)
p = 0.42p = 0.52
Country: Gh. × Porosity Vignettes:Imaginative involvement-0.10 (-0.22, 0.02)-0.10 (-0.22, 0.02)
p = 0.11p = 0.12
Country: Th. × Porosity Vignettes:Imaginative involvement0.09 (-0.05, 0.22)0.08 (-0.05, 0.22)
p = 0.22p = 0.24
Country: Ch. × Porosity Vignettes:Imaginative involvement0.14 (-0.04, 0.32)0.12 (-0.06, 0.31)
p = 0.14p = 0.19
Country: Va. × Porosity Vignettes:Imaginative involvement-0.01 (-0.16, 0.14)-0.01 (-0.17, 0.16)
p = 0.89p = 0.93
Scale: Spiritual Events × Country: Gh. × Porosity Vignettes:Imaginative involvement-0.10 (-0.18, -0.02)-0.10 (-0.18, -0.03)
p = 0.01p = 0.01
Scale: Spiritual Events × Country: Th. × Porosity Vignettes:Imaginative involvement0.07 (-0.01, 0.16)0.07 (-0.02, 0.16)
p = 0.11p = 0.13
Scale: Spiritual Events × Country: Ch. × Porosity Vignettes:Imaginative involvement-0.01 (-0.12, 0.11)-0.01 (-0.12, 0.11)
p = 0.93p = 0.90
Scale: Spiritual Events × Country: Va. × Porosity Vignettes:Imaginative involvement-0.03 (-0.12, 0.07)-0.03 (-0.13, 0.08)
p = 0.60p = 0.62
Scale: Spiritual Events × Porosity Scale:Imaginative involvement0.01 (-0.04, 0.06)0.01 (-0.04, 0.06)
p = 0.77p = 0.75
Country: Gh. × Porosity Scale:Imaginative involvement-0.13 (-0.26, -0.003)-0.12 (-0.25, 0.01)
p = 0.05p = 0.07
Country: Th. × Porosity Scale:Imaginative involvement0.05 (-0.08, 0.18)0.04 (-0.09, 0.17)
p = 0.49p = 0.54
Country: Ch. × Porosity Scale:Imaginative involvement0.05 (-0.12, 0.22)0.04 (-0.13, 0.21)
p = 0.56p = 0.66
Country: Va. × Porosity Scale:Imaginative involvement0.11 (-0.08, 0.30)0.11 (-0.08, 0.31)
p = 0.27p = 0.25
Scale: Spiritual Events × Country: Gh. × Porosity Scale:Imaginative involvement-0.08 (-0.16, 0.003)-0.08 (-0.17, 0.003)
p = 0.06p = 0.06
Scale: Spiritual Events × Country: Th. × Porosity Scale:Imaginative involvement0.02 (-0.07, 0.11)0.02 (-0.07, 0.11)
p = 0.65p = 0.64
Scale: Spiritual Events × Country: Ch. × Porosity Scale:Imaginative involvement0.04 (-0.07, 0.15)0.03 (-0.08, 0.15)
p = 0.53p = 0.55
Scale: Spiritual Events × Country: Va. × Porosity Scale:Imaginative involvement0.03 (-0.09, 0.16)0.03 (-0.10, 0.16)
p = 0.63p = 0.64
Constant-0.002 (-0.08, 0.08)0.02 (-0.04, 0.07)0.07 (0.001, 0.13)0.07 (-0.005, 0.14)0.06 (-0.01, 0.13)0.07 (-0.003, 0.15)
p = 0.97p = 0.58p = 0.05p = 0.07p = 0.09p = 0.06
Observations1,0081,0081,0081,008978978
Log Likelihood-1,288.13-1,132.79-1,105.55-1,094.29-1,083.67-1,074.74
Akaike Inf. Crit.2,588.252,309.572,295.102,272.582,255.342,237.49
Bayesian Inf. Crit.2,617.752,417.722,501.562,479.042,470.302,452.45
table_s40s41_subscales_MR5 <- stargazer(r105_spirit_MR5, r108_spirit_MR5, 
                                        r109_spirit_MR5, r110_spirit_MR5,
                                        r111_spirit_MR5, r112_spirit_MR5,
                                        type = "html",
                                        ci = T,
                                        report = c("vcsp"),
                                        digits = 2,
                                        digits.extra = 1000,
                                        omit.table.layout = "n",
                                        order = c("^`Sensed presence`$",
                                                  "^`Porosity Vignettes`$",
                                                  "^`Porosity Scale`$",
                                                  "^`Porosity Vignettes`:`Sensed presence`$",
                                                  "^`Porosity Scale`:`Sensed presence`$"),
                                        # dep.var.labels.include = F,
                                        dep.var.caption = "",
                                        single.row = T) 
cat(sub_interact_fun(table_s40s41_subscales_MR5), 
    file = "./stargazer_tables/table_s40s41_subscales_MR5.html")
htmltools::includeHTML("./stargazer_tables/table_s40s41_subscales_MR5.html")
Score
(1)(2)(3)(4)(5)(6)
Sensed presence0.14 (0.06, 0.22)0.14 (0.08, 0.19)0.13 (0.07, 0.20)0.10 (0.03, 0.17)0.14 (0.07, 0.20)0.10 (0.03, 0.17)
p = 0.001p = 0.000004p = 0.0001p = 0.01p = 0.0001p = 0.01
Porosity Vignettes0.24 (0.17, 0.31)0.24 (0.17, 0.31)
p = 0.00p = 0.00
Porosity Scale0.34 (0.27, 0.41)0.34 (0.27, 0.42)
p = 0.00p = 0.00
Porosity Vignettes:Sensed presence-0.02 (-0.09, 0.04)-0.02 (-0.09, 0.05)
p = 0.52p = 0.58
Porosity Scale:Sensed presence-0.02 (-0.09, 0.06)-0.02 (-0.10, 0.05)
p = 0.65p = 0.59
Scale: Spiritual Events-0.001 (-0.04, 0.03)-0.001 (-0.03, 0.03)-0.02 (-0.06, 0.03)-0.01 (-0.06, 0.04)-0.02 (-0.06, 0.03)-0.004 (-0.05, 0.05)
p = 0.94p = 0.97p = 0.46p = 0.83p = 0.49p = 0.90
Country: Gh.0.83 (0.71, 0.94)0.73 (0.60, 0.86)0.66 (0.53, 0.80)0.70 (0.57, 0.83)0.63 (0.49, 0.77)
p = 0.00p = 0.00p = 0.00p = 0.00p = 0.00
Country: Th.-0.29 (-0.40, -0.18)-0.30 (-0.41, -0.19)-0.25 (-0.37, -0.14)-0.31 (-0.42, -0.19)-0.25 (-0.37, -0.12)
p = 0.0000004p = 0.0000001p = 0.00002p = 0.0000002p = 0.0001
Country: Ch.-0.86 (-0.98, -0.75)-0.71 (-0.87, -0.56)-0.61 (-0.77, -0.45)-0.69 (-0.85, -0.53)-0.60 (-0.76, -0.43)
p = 0.00p = 0.00p = 0.00p = 0.00p = 0.00
Country: Va.0.62 (0.51, 0.74)0.51 (0.37, 0.66)0.38 (0.19, 0.58)0.48 (0.32, 0.63)0.35 (0.16, 0.55)
p = 0.00p = 0.00p = 0.0001p = 0.00p = 0.0005
Gender: male-0.02 (-0.08, 0.04)0.01 (-0.04, 0.07)
p = 0.47p = 0.65
Age0.07 (0.001, 0.13)0.05 (-0.01, 0.11)
p = 0.05p = 0.13
Scale: Spiritual Events × Country: Gh.-0.02 (-0.08, 0.05)-0.07 (-0.15, 0.02)-0.09 (-0.18, 0.002)-0.07 (-0.15, 0.02)-0.09 (-0.18, 0.002)
p = 0.66p = 0.13p = 0.06p = 0.13p = 0.06
Scale: Spiritual Events × Country: Th.-0.12 (-0.18, -0.05)-0.11 (-0.18, -0.03)-0.13 (-0.21, -0.05)-0.11 (-0.18, -0.03)-0.13 (-0.21, -0.05)
p = 0.001p = 0.004p = 0.002p = 0.004p = 0.002
Scale: Spiritual Events × Country: Ch.0.20 (0.13, 0.27)0.16 (0.06, 0.26)0.14 (0.03, 0.25)0.16 (0.06, 0.26)0.14 (0.02, 0.25)
p = 0.00p = 0.002p = 0.02p = 0.003p = 0.02
Scale: Spiritual Events × Country: Va.-0.14 (-0.21, -0.07)-0.10 (-0.20, -0.01)-0.01 (-0.14, 0.12)-0.10 (-0.20, -0.01)-0.004 (-0.14, 0.13)
p = 0.0002p = 0.03p = 0.85p = 0.04p = 0.95
Scale: Spiritual Events × Porosity Vignettes0.03 (-0.01, 0.08)0.04 (-0.01, 0.08)
p = 0.13p = 0.12
Country: Gh. × Porosity Vignettes-0.13 (-0.25, -0.02)-0.13 (-0.25, -0.02)
p = 0.03p = 0.03
Country: Th. × Porosity Vignettes-0.05 (-0.17, 0.08)-0.06 (-0.18, 0.07)
p = 0.48p = 0.38
Country: Ch. × Porosity Vignettes0.06 (-0.11, 0.23)0.04 (-0.13, 0.21)
p = 0.48p = 0.65
Country: Va. × Porosity Vignettes-0.14 (-0.29, 0.01)-0.14 (-0.29, 0.02)
p = 0.08p = 0.09
Scale: Spiritual Events × Porosity Scale-0.01 (-0.06, 0.03)-0.02 (-0.06, 0.03)
p = 0.58p = 0.54
Country: Gh. × Porosity Scale-0.12 (-0.25, 0.01)-0.12 (-0.25, 0.01)
p = 0.08p = 0.08
Country: Th. × Porosity Scale-0.02 (-0.15, 0.11)-0.03 (-0.16, 0.11)
p = 0.74p = 0.69
Country: Ch. × Porosity Scale0.04 (-0.11, 0.20)0.03 (-0.13, 0.19)
p = 0.58p = 0.71
Country: Va. × Porosity Scale-0.15 (-0.33, 0.03)-0.14 (-0.32, 0.04)
p = 0.11p = 0.13
Scale: Spiritual Events:Sensed presence0.01 (-0.02, 0.05)0.01 (-0.02, 0.04)0.01 (-0.03, 0.05)-0.003 (-0.05, 0.05)0.01 (-0.03, 0.05)-0.004 (-0.05, 0.05)
p = 0.56p = 0.56p = 0.58p = 0.92p = 0.61p = 0.87
Country: Gh. × Sensed presence0.04 (-0.07, 0.16)0.05 (-0.07, 0.16)0.05 (-0.07, 0.17)0.05 (-0.07, 0.16)0.05 (-0.07, 0.17)
p = 0.49p = 0.45p = 0.45p = 0.45p = 0.41
Country: Th. × Sensed presence0.03 (-0.08, 0.15)0.02 (-0.09, 0.13)0.04 (-0.07, 0.15)0.02 (-0.09, 0.13)0.04 (-0.08, 0.15)
p = 0.55p = 0.68p = 0.50p = 0.71p = 0.53
Country: Ch. × Sensed presence0.03 (-0.09, 0.14)0.09 (-0.07, 0.26)0.06 (-0.10, 0.22)0.10 (-0.07, 0.26)0.06 (-0.11, 0.22)
p = 0.64p = 0.28p = 0.47p = 0.24p = 0.50
Country: Va. × Sensed presence-0.10 (-0.22, 0.01)-0.08 (-0.22, 0.05)-0.12 (-0.31, 0.07)-0.08 (-0.22, 0.07)-0.10 (-0.30, 0.09)
p = 0.09p = 0.22p = 0.22p = 0.31p = 0.31
Scale: Spiritual Events × Country: Gh. × Porosity Vignettes0.08 (0.01, 0.16)0.08 (0.01, 0.16)
p = 0.03p = 0.04
Scale: Spiritual Events × Country: Th. × Porosity Vignettes-0.01 (-0.09, 0.07)-0.01 (-0.09, 0.07)
p = 0.74p = 0.73
Scale: Spiritual Events × Country: Ch. × Porosity Vignettes-0.12 (-0.23, -0.01)-0.12 (-0.23, -0.01)
p = 0.04p = 0.04
Scale: Spiritual Events × Country: Va. × Porosity Vignettes-0.04 (-0.14, 0.05)-0.05 (-0.15, 0.05)
p = 0.37p = 0.35
Scale: Spiritual Events × Country: Gh. × Porosity Scale0.13 (0.05, 0.22)0.14 (0.05, 0.22)
p = 0.003p = 0.003
Scale: Spiritual Events × Country: Th. × Porosity Scale-0.01 (-0.10, 0.08)-0.01 (-0.10, 0.08)
p = 0.88p = 0.90
Scale: Spiritual Events × Country: Ch. × Porosity Scale-0.07 (-0.18, 0.03)-0.07 (-0.18, 0.04)
p = 0.18p = 0.20
Scale: Spiritual Events × Country: Va. × Porosity Scale-0.12 (-0.23, 0.003)-0.12 (-0.24, 0.001)
p = 0.06p = 0.06
Scale: Spiritual Events × Country: Gh. × Sensed presence-0.03 (-0.09, 0.04)-0.05 (-0.12, 0.03)-0.05 (-0.13, 0.03)-0.05 (-0.12, 0.03)-0.05 (-0.13, 0.03)
p = 0.47p = 0.23p = 0.22p = 0.25p = 0.24
Scale: Spiritual Events × Country: Th. × Sensed presence0.04 (-0.03, 0.11)0.05 (-0.02, 0.12)0.07 (-0.003, 0.15)0.05 (-0.02, 0.12)0.07 (-0.003, 0.15)
p = 0.24p = 0.19p = 0.06p = 0.20p = 0.06
Scale: Spiritual Events × Country: Ch. × Sensed presence-0.001 (-0.07, 0.07)-0.03 (-0.13, 0.08)-0.01 (-0.11, 0.10)-0.03 (-0.13, 0.08)-0.01 (-0.12, 0.10)
p = 0.98p = 0.60p = 0.91p = 0.61p = 0.93
Scale: Spiritual Events × Country: Va. × Sensed presence0.01 (-0.06, 0.09)0.07 (-0.01, 0.16)0.01 (-0.12, 0.14)0.07 (-0.03, 0.16)0.01 (-0.13, 0.14)
p = 0.70p = 0.09p = 0.84p = 0.16p = 0.91
Scale: Spiritual Events × Porosity Vignettes:Sensed presence-0.02 (-0.07, 0.02)-0.02 (-0.07, 0.02)
p = 0.28p = 0.33
Country: Gh. × Porosity Vignettes:Sensed presence-0.06 (-0.18, 0.05)-0.06 (-0.18, 0.06)
p = 0.29p = 0.35
Country: Th. × Porosity Vignettes:Sensed presence0.08 (-0.04, 0.19)0.07 (-0.05, 0.18)
p = 0.19p = 0.25
Country: Ch. × Porosity Vignettes:Sensed presence0.14 (-0.03, 0.31)0.12 (-0.05, 0.30)
p = 0.12p = 0.16
Country: Va. × Porosity Vignettes:Sensed presence-0.03 (-0.17, 0.11)-0.04 (-0.18, 0.11)
p = 0.66p = 0.61
Scale: Spiritual Events × Country: Gh. × Porosity Vignettes:Sensed presence-0.005 (-0.08, 0.07)-0.01 (-0.08, 0.07)
p = 0.91p = 0.88
Scale: Spiritual Events × Country: Th. × Porosity Vignettes:Sensed presence0.11 (0.04, 0.19)0.11 (0.04, 0.19)
p = 0.002p = 0.003
Scale: Spiritual Events × Country: Ch. × Porosity Vignettes:Sensed presence-0.02 (-0.13, 0.09)-0.02 (-0.13, 0.09)
p = 0.71p = 0.71
Scale: Spiritual Events × Country: Va. × Porosity Vignettes:Sensed presence-0.10 (-0.19, -0.01)-0.09 (-0.19, 0.0005)
p = 0.04p = 0.06
Scale: Spiritual Events × Porosity Scale:Sensed presence0.02 (-0.03, 0.07)0.02 (-0.03, 0.07)
p = 0.45p = 0.50
Country: Gh. × Porosity Scale:Sensed presence-0.06 (-0.19, 0.06)-0.05 (-0.18, 0.08)
p = 0.34p = 0.43
Country: Th. × Porosity Scale:Sensed presence0.03 (-0.10, 0.17)0.04 (-0.10, 0.17)
p = 0.65p = 0.60
Country: Ch. × Porosity Scale:Sensed presence0.07 (-0.08, 0.22)0.06 (-0.09, 0.22)
p = 0.37p = 0.45
Country: Va. × Porosity Scale:Sensed presence0.07 (-0.14, 0.27)0.05 (-0.16, 0.26)
p = 0.53p = 0.64
Scale: Spiritual Events × Country: Gh. × Porosity Scale:Sensed presence0.02 (-0.07, 0.10)0.02 (-0.07, 0.11)
p = 0.68p = 0.66
Scale: Spiritual Events × Country: Th. × Porosity Scale:Sensed presence0.08 (-0.01, 0.17)0.08 (-0.01, 0.17)
p = 0.08p = 0.08
Scale: Spiritual Events × Country: Ch. × Porosity Scale:Sensed presence-0.05 (-0.16, 0.05)-0.05 (-0.16, 0.05)
p = 0.30p = 0.32
Scale: Spiritual Events × Country: Va. × Porosity Scale:Sensed presence0.01 (-0.13, 0.15)0.01 (-0.13, 0.15)
p = 0.89p = 0.94
Constant-0.001 (-0.08, 0.08)0.02 (-0.04, 0.07)0.07 (0.004, 0.14)0.07 (-0.001, 0.15)0.06 (-0.01, 0.13)0.07 (-0.0001, 0.15)
p = 0.98p = 0.58p = 0.04p = 0.06p = 0.08p = 0.06
Observations1,0081,0081,0081,008978978
Log Likelihood-1,284.75-1,130.40-1,106.45-1,094.71-1,085.16-1,075.08
Akaike Inf. Crit.2,581.492,304.792,296.912,273.412,258.332,238.15
Bayesian Inf. Crit.2,610.992,412.942,503.372,479.872,473.292,453.11
table_s40s41_subscales_MR6 <- stargazer(r105_spirit_MR6, r108_spirit_MR6, 
                                        r109_spirit_MR6, r110_spirit_MR6,
                                        r111_spirit_MR6, r112_spirit_MR6,
                                        type = "html",
                                        ci = T,
                                        report = c("vcsp"),
                                        digits = 2,
                                        digits.extra = 1000,
                                        omit.table.layout = "n",
                                        order = c("^`Altered states of consciousness \\(b\\)`$",
                                                  "^`Porosity Vignettes`$",
                                                  "^`Porosity Scale`$",
                                                  "^`Porosity Vignettes`:`Altered states of consciousness \\(b\\)`$",
                                                  "^`Porosity Scale`:`Altered states of consciousness \\(b\\)`$"),
                                        # dep.var.labels.include = F,
                                        dep.var.caption = "",
                                        single.row = T) 
cat(sub_interact_fun(table_s40s41_subscales_MR6), 
    file = "./stargazer_tables/table_s40s41_subscales_MR6.html")
htmltools::includeHTML("./stargazer_tables/table_s40s41_subscales_MR6.html")
Score
(1)(2)(3)(4)(5)(6)
Altered states of consciousness (b)0.16 (0.08, 0.24)0.16 (0.10, 0.21)0.14 (0.07, 0.21)0.12 (0.05, 0.19)0.14 (0.07, 0.21)0.13 (0.05, 0.20)
p = 0.0001p = 0.0000001p = 0.0001p = 0.0005p = 0.00004p = 0.001
Porosity Vignettes0.25 (0.18, 0.32)0.25 (0.18, 0.32)
p = 0.00p = 0.00
Porosity Scale0.33 (0.25, 0.40)0.33 (0.26, 0.41)
p = 0.00p = 0.00
Porosity Vignettes:Altered states of consciousness (b)0.03 (-0.03, 0.10)0.04 (-0.02, 0.11)
p = 0.33p = 0.22
Porosity Scale:Altered states of consciousness (b)0.03 (-0.04, 0.10)0.04 (-0.03, 0.11)
p = 0.42p = 0.32
Scale: Spiritual Events-0.002 (-0.04, 0.03)-0.001 (-0.03, 0.03)-0.02 (-0.07, 0.02)-0.001 (-0.05, 0.05)-0.02 (-0.06, 0.02)0.003 (-0.05, 0.05)
p = 0.94p = 0.97p = 0.30p = 0.97p = 0.36p = 0.92
Country: Gh.0.83 (0.71, 0.94)0.68 (0.56, 0.81)0.61 (0.48, 0.74)0.67 (0.54, 0.80)0.58 (0.45, 0.72)
p = 0.00p = 0.00p = 0.00p = 0.00p = 0.00
Country: Th.-0.29 (-0.40, -0.18)-0.32 (-0.43, -0.21)-0.27 (-0.39, -0.15)-0.32 (-0.44, -0.20)-0.27 (-0.39, -0.14)
p = 0.0000004p = 0.00000003p = 0.00001p = 0.0000001p = 0.00003
Country: Ch.-0.86 (-0.98, -0.75)-0.65 (-0.80, -0.49)-0.60 (-0.76, -0.44)-0.62 (-0.78, -0.46)-0.59 (-0.75, -0.42)
p = 0.00p = 0.00p = 0.00p = 0.00p = 0.00
Country: Va.0.62 (0.51, 0.74)0.52 (0.37, 0.66)0.46 (0.26, 0.66)0.50 (0.35, 0.66)0.44 (0.24, 0.65)
p = 0.00p = 0.00p = 0.00001p = 0.00p = 0.00003
Gender: male-0.02 (-0.08, 0.04)0.01 (-0.05, 0.07)
p = 0.46p = 0.68
Age0.05 (-0.01, 0.11)0.04 (-0.02, 0.11)
p = 0.13p = 0.18
Scale: Spiritual Events × Country: Gh.-0.02 (-0.08, 0.05)-0.05 (-0.13, 0.03)-0.07 (-0.15, 0.02)-0.05 (-0.14, 0.03)-0.07 (-0.16, 0.02)
p = 0.64p = 0.22p = 0.13p = 0.21p = 0.12
Scale: Spiritual Events × Country: Th.-0.12 (-0.18, -0.05)-0.10 (-0.17, -0.03)-0.14 (-0.21, -0.06)-0.10 (-0.17, -0.03)-0.14 (-0.22, -0.06)
p = 0.0005p = 0.01p = 0.001p = 0.01p = 0.001
Scale: Spiritual Events × Country: Ch.0.20 (0.13, 0.27)0.16 (0.06, 0.26)0.12 (0.02, 0.23)0.16 (0.06, 0.26)0.12 (0.01, 0.23)
p = 0.00p = 0.002p = 0.03p = 0.003p = 0.04
Scale: Spiritual Events × Country: Va.-0.14 (-0.21, -0.07)-0.12 (-0.21, -0.02)0.02 (-0.12, 0.15)-0.11 (-0.21, -0.01)0.03 (-0.11, 0.17)
p = 0.0002p = 0.02p = 0.81p = 0.03p = 0.66
Scale: Spiritual Events × Porosity Vignettes0.03 (-0.02, 0.07)0.03 (-0.02, 0.07)
p = 0.21p = 0.22
Country: Gh. × Porosity Vignettes-0.12 (-0.23, 0.001)-0.12 (-0.24, -0.0005)
p = 0.06p = 0.05
Country: Th. × Porosity Vignettes-0.08 (-0.20, 0.05)-0.09 (-0.21, 0.04)
p = 0.24p = 0.19
Country: Ch. × Porosity Vignettes0.12 (-0.05, 0.29)0.10 (-0.07, 0.27)
p = 0.18p = 0.24
Country: Va. × Porosity Vignettes-0.16 (-0.31, -0.01)-0.16 (-0.32, -0.01)
p = 0.04p = 0.04
Scale: Spiritual Events × Porosity Scale-0.03 (-0.08, 0.01)-0.04 (-0.08, 0.01)
p = 0.18p = 0.17
Country: Gh. × Porosity Scale-0.07 (-0.19, 0.06)-0.07 (-0.19, 0.06)
p = 0.31p = 0.31
Country: Th. × Porosity Scale-0.02 (-0.16, 0.11)-0.03 (-0.17, 0.10)
p = 0.74p = 0.66
Country: Ch. × Porosity Scale0.08 (-0.08, 0.23)0.07 (-0.09, 0.22)
p = 0.33p = 0.40
Country: Va. × Porosity Scale-0.21 (-0.40, -0.03)-0.22 (-0.40, -0.03)
p = 0.03p = 0.03
Scale: Spiritual Events:Altered states of consciousness (b)0.06 (0.02, 0.09)0.06 (0.02, 0.09)0.05 (0.01, 0.10)0.09 (0.04, 0.14)0.05 (0.01, 0.10)0.09 (0.04, 0.14)
p = 0.002p = 0.002p = 0.02p = 0.0002p = 0.02p = 0.0002
Country: Gh. × Altered states of consciousness (b)-0.07 (-0.18, 0.05)-0.09 (-0.22, 0.03)-0.07 (-0.20, 0.05)-0.09 (-0.22, 0.04)-0.08 (-0.21, 0.04)
p = 0.24p = 0.15p = 0.26p = 0.17p = 0.20
Country: Th. × Altered states of consciousness (b)-0.01 (-0.12, 0.10)-0.02 (-0.13, 0.09)-0.01 (-0.12, 0.10)-0.02 (-0.13, 0.09)-0.005 (-0.12, 0.11)
p = 0.84p = 0.77p = 0.90p = 0.77p = 0.94
Country: Ch. × Altered states of consciousness (b)-0.06 (-0.18, 0.05)0.08 (-0.09, 0.24)-0.01 (-0.17, 0.15)0.07 (-0.09, 0.23)-0.02 (-0.17, 0.14)
p = 0.30p = 0.36p = 0.91p = 0.39p = 0.83
Country: Va. × Altered states of consciousness (b)0.03 (-0.09, 0.15)0.11 (-0.03, 0.26)0.09 (-0.09, 0.27)0.11 (-0.03, 0.26)0.09 (-0.08, 0.27)
p = 0.64p = 0.14p = 0.32p = 0.13p = 0.31
Scale: Spiritual Events × Country: Gh. × Porosity Vignettes0.07 (-0.0001, 0.15)0.07 (-0.002, 0.15)
p = 0.06p = 0.06
Scale: Spiritual Events × Country: Th. × Porosity Vignettes-0.01 (-0.09, 0.07)-0.01 (-0.10, 0.07)
p = 0.73p = 0.73
Scale: Spiritual Events × Country: Ch. × Porosity Vignettes-0.11 (-0.22, -0.0005)-0.11 (-0.22, 0.002)
p = 0.05p = 0.06
Scale: Spiritual Events × Country: Va. × Porosity Vignettes-0.02 (-0.11, 0.08)-0.03 (-0.13, 0.08)
p = 0.75p = 0.63
Scale: Spiritual Events × Country: Gh. × Porosity Scale0.13 (0.05, 0.22)0.14 (0.05, 0.22)
p = 0.002p = 0.002
Scale: Spiritual Events × Country: Th. × Porosity Scale-0.01 (-0.09, 0.08)-0.01 (-0.10, 0.08)
p = 0.89p = 0.90
Scale: Spiritual Events × Country: Ch. × Porosity Scale-0.06 (-0.16, 0.04)-0.06 (-0.16, 0.05)
p = 0.27p = 0.31
Scale: Spiritual Events × Country: Va. × Porosity Scale-0.12 (-0.24, 0.004)-0.13 (-0.25, -0.01)
p = 0.06p = 0.05
Scale: Spiritual Events × Country: Gh. × Altered states of consciousness (b)0.01 (-0.06, 0.08)0.001 (-0.08, 0.08)0.01 (-0.07, 0.10)-0.001 (-0.08, 0.08)0.01 (-0.07, 0.10)
p = 0.83p = 0.99p = 0.75p = 0.99p = 0.81
Scale: Spiritual Events × Country: Th. × Altered states of consciousness (b)0.0005 (-0.06, 0.07)0.002 (-0.07, 0.07)-0.02 (-0.09, 0.05)0.001 (-0.07, 0.07)-0.02 (-0.10, 0.05)
p = 0.99p = 0.96p = 0.63p = 0.99p = 0.59
Scale: Spiritual Events × Country: Ch. × Altered states of consciousness (b)-0.04 (-0.11, 0.03)-0.07 (-0.17, 0.04)-0.06 (-0.16, 0.04)-0.07 (-0.17, 0.04)-0.06 (-0.17, 0.04)
p = 0.23p = 0.21p = 0.26p = 0.20p = 0.25
Scale: Spiritual Events × Country: Va. × Altered states of consciousness (b)-0.03 (-0.10, 0.04)0.03 (-0.06, 0.12)0.03 (-0.09, 0.14)0.03 (-0.06, 0.13)0.03 (-0.09, 0.15)
p = 0.41p = 0.55p = 0.66p = 0.51p = 0.66
Scale: Spiritual Events × Porosity Vignettes:Altered states of consciousness (b)-0.01 (-0.05, 0.03)-0.01 (-0.05, 0.03)
p = 0.62p = 0.63
Country: Gh. × Porosity Vignettes:Altered states of consciousness (b)-0.004 (-0.12, 0.12)-0.01 (-0.14, 0.11)
p = 0.96p = 0.81
Country: Th. × Porosity Vignettes:Altered states of consciousness (b)0.09 (-0.02, 0.21)0.08 (-0.03, 0.20)
p = 0.11p = 0.17
Country: Ch. × Porosity Vignettes:Altered states of consciousness (b)0.09 (-0.07, 0.26)0.07 (-0.10, 0.24)
p = 0.27p = 0.43
Country: Va. × Porosity Vignettes:Altered states of consciousness (b)-0.14 (-0.28, 0.005)-0.15 (-0.30, -0.01)
p = 0.06p = 0.04
Scale: Spiritual Events × Country: Gh. × Porosity Vignettes:Altered states of consciousness (b)-0.01 (-0.09, 0.07)-0.01 (-0.09, 0.07)
p = 0.83p = 0.83
Scale: Spiritual Events × Country: Th. × Porosity Vignettes:Altered states of consciousness (b)0.06 (-0.01, 0.14)0.06 (-0.01, 0.14)
p = 0.10p = 0.11
Scale: Spiritual Events × Country: Ch. × Porosity Vignettes:Altered states of consciousness (b)-0.02 (-0.13, 0.09)-0.02 (-0.13, 0.09)
p = 0.72p = 0.74
Scale: Spiritual Events × Country: Va. × Porosity Vignettes:Altered states of consciousness (b)-0.07 (-0.16, 0.02)-0.07 (-0.17, 0.02)
p = 0.13p = 0.13
Scale: Spiritual Events × Porosity Scale:Altered states of consciousness (b)-0.01 (-0.05, 0.04)-0.004 (-0.05, 0.04)
p = 0.75p = 0.86
Country: Gh. × Porosity Scale:Altered states of consciousness (b)0.004 (-0.12, 0.13)0.002 (-0.13, 0.13)
p = 0.95p = 0.98
Country: Th. × Porosity Scale:Altered states of consciousness (b)0.06 (-0.07, 0.18)0.05 (-0.07, 0.18)
p = 0.39p = 0.43
Country: Ch. × Porosity Scale:Altered states of consciousness (b)0.04 (-0.11, 0.18)0.02 (-0.13, 0.16)
p = 0.62p = 0.83
Country: Va. × Porosity Scale:Altered states of consciousness (b)-0.08 (-0.25, 0.09)-0.09 (-0.26, 0.08)
p = 0.34p = 0.31
Scale: Spiritual Events × Country: Gh. × Porosity Scale:Altered states of consciousness (b)-0.08 (-0.16, 0.01)-0.08 (-0.17, 0.01)
p = 0.08p = 0.07
Scale: Spiritual Events × Country: Th. × Porosity Scale:Altered states of consciousness (b)0.08 (-0.01, 0.16)0.07 (-0.01, 0.16)
p = 0.07p = 0.09
Scale: Spiritual Events × Country: Ch. × Porosity Scale:Altered states of consciousness (b)0.02 (-0.08, 0.11)0.02 (-0.08, 0.11)
p = 0.72p = 0.76
Scale: Spiritual Events × Country: Va. × Porosity Scale:Altered states of consciousness (b)-0.07 (-0.18, 0.04)-0.07 (-0.19, 0.04)
p = 0.21p = 0.22
Constant-0.002 (-0.08, 0.08)0.02 (-0.04, 0.07)0.07 (0.004, 0.14)0.08 (0.004, 0.15)0.06 (-0.01, 0.13)0.08 (0.01, 0.16)
p = 0.98p = 0.58p = 0.04p = 0.04p = 0.09p = 0.04
Observations1,0081,0081,0081,008978978
Log Likelihood-1,278.27-1,119.88-1,109.91-1,091.10-1,086.86-1,070.19
Akaike Inf. Crit.2,568.552,283.762,303.822,266.192,261.722,228.38
Bayesian Inf. Crit.2,598.042,391.912,510.282,472.652,476.682,443.34
---
title: "Reply to Terhune et al."
subtitle: "Luhrmann, Weisman, et al."
output: 
  html_notebook:
    toc: true
    toc_float: true
    code_folding: hide
---

In Study 1, adults with strong religious commitments and faith practices were interviewed in depth about their spiritual experiences and their understanding of the mind by experienced ethnographers. These interviews yielded one measure of porosity (“Porosity Vignettes”), one measure of absorption (the Absorption scale), and one measure of spiritual experience (“Spiritual Events” scale).

This notebook contains all of the analyses for Study 1 (revised July 2020).

```{r, message = F}
source("../scripts_general/dependencies.R")
source("../scripts_general/custom_funs.R")
source("../scripts_general/var_recode_contrast.R")
source("../scripts_general/data_load.R")
```

```{r}
# get item-level question
d1_byq <- read_csv("./data_byquestion/study1_byquestion.csv")
d3_byq <- read_csv("./data_byquestion/packets123_data_byquestion_long.csv") %>%
  filter(packet == 1)
d4_byq <- read_csv("./data_byquestion/study4_byquestion.csv")
```

```{r}
# dataframe with variable names optimized for stargazer tables
d1_fortab <- d1 %>%
  mutate_at(vars(ends_with("_score"), subject_age), scale) %>%
  mutate(subject_hs = case_when(is.na(subject_hs) ~ NA_character_,
                                grepl("yes", tolower(subject_hs)) ~ "yes",
                                TRUE ~ "no"),
         subject_hs = factor(subject_hs, 
                             levels = c("no", "yes")),
         subject_gender = factor(subject_gender, 
                                 levels = c("Female", "Male"))) %>%
  rename("Spiritual Events" = "spev_score",
         "Spiritual Events, recoded" = "spev_recoded_score",
         "Porosity Vignettes" = "pv_score",
         "Absorption" = "abs_score",
         "Country" = "country",
         "Site" = "site",
         "Religion" = "religion",
         "Gender" = "subject_gender",
         "Age" = "subject_age",
         "Education" = "subject_hs")
```

```{r}
# set contrasts
contrasts(d1_fortab$Country) <- cbind(": Gh." = c(-1, 1, 0, 0, 0),
                                      ": Th." = c(-1, 0, 1, 0, 0),
                                      ": Ch." = c(-1, 0, 0, 1, 0),
                                      ": Va." = c(-1, 0, 0, 0, 1))
contrasts(d1_fortab$Site) <- cbind(": rural" = c(-1, 1))
contrasts(d1_fortab$Religion) <- cbind(": CC" = c(1, -1))
contrasts(d1_fortab$Education) <- cbind(": at least HS" = c(-1, 1))
contrasts(d1_fortab$Gender) <- cbind(": male" = c(-1, 1))
```

```{r}
# dataframe with variable names optimized for stargazer tables
d3_fortab <- d3 %>%
  mutate_at(vars(ends_with("_score"), subject_age), scale) %>%
  mutate(subject_gender = factor(subject_gender, 
                                 levels = c("female", "male"))) %>%
  rename("Spiritual Events" = "spev_score",
         "Daily Spiritual Experiences" = "dse_score",
         "Absorption" = "abs_score",
         "Country" = "country",
         "Gender" = "subject_gender",
         "Age" = "subject_age")
```

```{r}
# set contrasts
contrasts(d3_fortab$Country) <- cbind(": Gh." = c(-1, 1, 0, 0, 0),
                                      ": Th." = c(-1, 0, 1, 0, 0),
                                      ": Ch." = c(-1, 0, 0, 1, 0),
                                      ": Va." = c(-1, 0, 0, 0, 1))
contrasts(d3_fortab$Gender) <- cbind(": male" = c(-1, 1))
```

```{r}
# dataframe with variable names optimized for stargazer tables
d4_fortab <- d4 %>%
  mutate_at(vars(ends_with("_score"), subject_age), scale) %>%
  mutate(subject_gender = factor(subject_gender, 
                                 levels = c("female", "male"))) %>%
  rename("Spiritual Events" = "spev_score",
         "Daily Spiritual Experience" = "dse_score",
         "Porosity Vignettes" = "pv_score",
         "Porosity Scale" = "por_score",
         "Absorption" = "abs_score",
         "Hallucinations" = "hall_score",
         "Paranormal" = "para_score",
         "Need for Cognition" = "cog_score",
         "Sense of Control" = "ctl_score",
         "Country" = "country",
         "Gender" = "subject_gender",
         "Age" = "subject_age")
```

```{r}
# set contrasts
contrasts(d4_fortab$Country) <- cbind(": Gh." = c(-1, 1, 0, 0, 0),
                                      ": Th." = c(-1, 0, 1, 0, 0),
                                      ": Ch." = c(-1, 0, 0, 1, 0),
                                      ": Va." = c(-1, 0, 0, 0, 1))
contrasts(d4_fortab$Gender) <- cbind(": male" = c(-1, 1))
```

# Omitting two presence items

"I can often somehow sense the presence of another person before I actually see or hear her/him."
"At times I somehow feel the presence of someone who is not physically there"

## Study 1

```{r}
d1_abs_omit <- d1_byq %>%
  select(subject_id, country, ends_with("_abs")) %>%
  select(-sensepresence_abs, -invisiblepresence_abs) %>%
  gather(question, response, -subject_id, -country) %>%
  group_by(subject_id, country) %>%
  summarise(Absorption = mean(response, na.rm = T)) %>%
  ungroup() %>%
  group_by(country) %>%
  mutate(Absorption = scale(Absorption)) %>%
  ungroup() %>%
  distinct(subject_id, Absorption) %>%
  right_join(d1_fortab %>% select(-Absorption)) %>%
  filter(subject_id %in% d1_fortab$subject_id)
```

### Table S20. Study 1, Spiritual Events: relationships with Porosity Vignettes and Absorption (sample as fixed effect)

```{r}
r04_spev <- lm(`Spiritual Events` ~ `Porosity Vignettes`,
               data = d1_abs_omit)

r05_spev <- lm(`Spiritual Events` ~ Absorption,
               data = d1_abs_omit)

r06_spev <- lm(`Spiritual Events` ~ `Porosity Vignettes` * Absorption,
               data = d1_abs_omit)

r07_spev <- lm(`Spiritual Events` ~ Country * Site * Religion 
               + `Porosity Vignettes` + Country:`Porosity Vignettes`,
               data = d1_abs_omit)

r08_spev <- lm(`Spiritual Events` ~ Country * Site * Religion 
               + Absorption + Country:Absorption,
               data = d1_abs_omit)

r09_spev <- lm(`Spiritual Events` ~ Country * Site * Religion 
               + `Porosity Vignettes` + Country:`Porosity Vignettes`
               + Absorption + Country:Absorption
               + `Porosity Vignettes`:Absorption 
               + Country:`Porosity Vignettes`:Absorption,
               data = d1_abs_omit)

r10_spev <- lm(`Spiritual Events` ~ Country * Site * Religion 
               + `Porosity Vignettes` + Country:`Porosity Vignettes`
               + Absorption + Country:Absorption
               + `Porosity Vignettes`:Absorption
               + Country:`Porosity Vignettes`:Absorption
               + Gender + Age + Education,
               data = d1_abs_omit)
```

```{r, include = F}
# run manually to check convergence
summary(r04_spev)
summary(r05_spev)
summary(r06_spev)
summary(r07_spev)
summary(r08_spev)
summary(r09_spev)
summary(r10_spev)
```

```{r, results = "hide"}
table_s20 <- stargazer(r04_spev, r05_spev, r06_spev, 
                       r07_spev, r08_spev, r09_spev, r10_spev,
                       type = "html",
                       ci = T,
                       report = c("vcsp"),
                       digits = 2,
                       digits.extra = 1000,
                       omit.table.layout = "n",
                       order = c("^`Porosity Vignettes`$",
                                 "^Absorption$",
                                 "^`Porosity Vignettes`:Absorption$"),
                       # dep.var.labels.include = F,
                       dep.var.caption = "",
                       single.row = T)

cat(sub_interact_fun(table_s20), 
    file = "./stargazer_tables/table_s20.html")
```

```{r}
htmltools::includeHTML("./stargazer_tables/table_s20.html")
```

### Table S21. Study 1, Spiritual Events: relationships with Porosity Vignettes and Absorption (sample as random effect)

```{r}
r11_spev <- lmer(`Spiritual Events` ~ 1 
                 + (1 | Country/Site/Religion),
                 control = lmerControl(optimizer = "bobyqa"),
                 data = d1_abs_omit)

r12_spev <- lmer(`Spiritual Events` ~ `Porosity Vignettes` 
                 # + (1 + `Porosity Vignettes` | Country/Site/Religion),
                 + (1 | Country/Site/Religion),
                 control = lmerControl(optimizer = "bobyqa"),
                 data = d1_abs_omit)

r13_spev <- lmer(`Spiritual Events` ~ Absorption
                 # + (1 + Absorption | Country/Site/Religion),
                 + (1 | Country/Site/Religion),
                 control = lmerControl(optimizer = "bobyqa"),
                 data = d1_abs_omit)

r14_spev <- lmer(`Spiritual Events` ~ `Porosity Vignettes` * Absorption 
                 # + (1 + `Porosity Vignettes` * Absorption | Country/Site/Religion),
                 # + (1 + `Porosity Vignettes` + Absorption | Country/Site/Religion),
                 + (1 | Country/Site/Religion),
                 control = lmerControl(optimizer = "bobyqa"),
                 data = d1_abs_omit)

r15_spev <- lmer(`Spiritual Events` ~ `Porosity Vignettes` * Absorption
                 + Gender + Age + Education
                 # + (1 + `Porosity Vignettes` * Absorption | Country/Site/Religion),
                 # + (1 + `Porosity Vignettes` + Absorption | Country/Site/Religion),
                 + (1 | Country/Site/Religion),
                 control = lmerControl(optimizer = "bobyqa"),
                 data = d1_abs_omit)
```

```{r, include = F}
# run manually to check convergence
summary(r11_spev)
summary(r12_spev)
summary(r13_spev)
summary(r14_spev)
summary(r15_spev)
```

```{r, include=F}
table_s21 <- stargazer(#r04_spev, r05_spev, r06_spev, 
  r11_spev, r12_spev, r13_spev, r14_spev, r15_spev,
  type = "html",
  ci = T,
  report = c("vcsp"),
  digits = 2,
  digits.extra = 1000,
  omit.table.layout = "n",
  order = c("^`Porosity Vignettes`$",
            "^Absorption$",
            "^`Porosity Vignettes`:Absorption$"),
  # dep.var.labels.include = F,
  dep.var.caption = "",
  single.row = T)

cat(sub_interact_fun(table_s21), 
    file = "./stargazer_tables/table_s21.html")
```

```{r}
htmltools::includeHTML("./stargazer_tables/table_s21.html")
```

```{r}
rsq_table_fun(list(r11_spev, r12_spev, r13_spev, r14_spev, r15_spev))
```


## Study 3

```{r}
d3_abs_omit <- d3_byq %>%
  select(subj, ctry, question, response) %>%
  filter(question %in% c("exwl_01", "exwl_02", "exwl_03", "exwl_04", "exwl_05", "exwl_06",
                         "exwl_07", "exwl_08", "exwl_09", "exwl_10", "exwl_11", "exwl_12",
                         "exwl_13", "exwl_14", "exwl_15", "exwl_16", "exwl_17", "exwl_18",
                         "exwl_19", "exwl_20", "exwl_21", "exwl_22", "exwl_23", "exwl_24",
                         "exwl_25", "exwl_26", "exwl_27", "exwl_28", "exwl_29", "exwl_30",
                         "exwl_31", "exwl_32", "exwl_33", "exwl_34")) %>%
  filter(question != "exwl_14", question != "exwl_31") %>%
  group_by(subj, ctry) %>%
  summarise(Absorption = mean(response, na.rm = T)) %>%
  ungroup() %>%
  group_by(ctry) %>%
  mutate(Absorption = scale(Absorption)) %>%
  ungroup() %>%
  distinct(subj, Absorption) %>%
  rename(subject_id = subj) %>%
  right_join(d3_fortab %>% select(-Absorption)) %>%
  filter(subject_id %in% d3_fortab$subject_id)
```

### Table S32. Study 3, Spiritual Events and Daily Spiritual Experience: relationships with Absorption (sample as fixed effect)

```{r}
r03_spev <- lm(`Spiritual Events` ~ `Absorption`,
               data = d3_abs_omit)

r04_spev <- lm(`Spiritual Events` ~ Country * `Absorption`,
               data = d3_abs_omit)

r05_spev <- lm(`Spiritual Events` ~ Country * `Absorption`
               + Gender + Age,  
               data = d3_abs_omit)
```

```{r, include = F}
# run manually to check convergence
summary(r03_spev)
summary(r04_spev)
summary(r05_spev)
```

```{r}
r03_dse <- lm(`Daily Spiritual Experiences` ~ `Absorption`,
              data = d3_abs_omit)

r04_dse <- lm(`Daily Spiritual Experiences` ~ Country * `Absorption`,
              data = d3_abs_omit)

r05_dse <- lm(`Daily Spiritual Experiences` ~ Country * `Absorption`
              + Gender + Age,  
              data = d3_abs_omit)
```

```{r, include = F}
# run manually to check convergence
summary(r03_dse)
summary(r04_dse)
summary(r05_dse)
```

```{r, results = "hide"}
table_s32 <- stargazer(r03_spev, r04_spev, r05_spev, r03_dse, r04_dse, r05_dse, 
                       type = "html",
                       ci = T,
                       report = c("vcsp"),
                       digits = 2,
                       digits.extra = 1000,
                       omit.table.layout = "n",
                       order = c("^Absorption$"),
                       # dep.var.labels.include = F,
                       dep.var.caption = "",
                       single.row = T)

cat(sub_interact_fun(table_s32), 
    file = "./stargazer_tables/table_s32.html")
```

```{r}
htmltools::includeHTML("./stargazer_tables/table_s32.html")
```

### Table S33. Study 3, Spiritual Events and Daily Spiritual Experience: relationships with Absorption (sample as random effect)

```{r}
r06_spev <- lmer(`Spiritual Events` ~ 1 
                 + (1 | Country),
                 control = lmerControl(optimizer = "bobyqa"),
                 data = d3_abs_omit)

r07_spev <- lmer(`Spiritual Events` ~ `Absorption` 
                 # + (1 + `Absorption` | Country), # perfectly correlated ranfx
                 + (1 | Country),
                 control = lmerControl(optimizer = "bobyqa"),
                 data = d3_abs_omit)

r08_spev <- lmer(`Spiritual Events` ~ `Absorption`
                 + Gender + Age 
                 # + (1 + `Absorption` + Gender + Age | Country), # perfectly correlated ranfx
                 # + (1 + `Absorption` | Country), # perfectly correlated ranfx
                 + (1 | Country),
                 control = lmerControl(optimizer = "bobyqa"),
                 data = d3_abs_omit)
```

```{r, include = F}
# run manually to check convergence
summary(r06_spev)
summary(r07_spev)
summary(r08_spev)
```

```{r}
r06_dse <- lmer(`Daily Spiritual Experiences` ~ 1 
                + (1 | Country),
                control = lmerControl(optimizer = "bobyqa"),
                data = d3_abs_omit)

r07_dse <- lmer(`Daily Spiritual Experiences` ~ `Absorption` 
                # + (1 + `Absorption` | Country), # perfectly correlated ranfx
                + (1 | Country),
                control = lmerControl(optimizer = "bobyqa"),
                data = d3_abs_omit)

r08_dse <- lmer(`Daily Spiritual Experiences` ~ `Absorption`
                + Gender + Age 
                # + (1 + `Absorption` + Gender + Age | Country), 
                # + (1 + `Absorption` | Country),
                + (1 | Country), # use this for comparsion with spev
                control = lmerControl(optimizer = "bobyqa"),
                data = d3_abs_omit)
```

```{r, include = F}
# run manually to check convergence
summary(r06_dse)
summary(r07_dse)
summary(r08_dse)
```

```{r, results = "hide"}
table_s33 <- stargazer(r06_spev, r07_spev, r08_spev, r06_dse, r07_dse, r08_dse,
                       type = "html",
                       ci = T,
                       report = c("vcsp"),
                       digits = 2,
                       digits.extra = 0800,
                       omit.table.layout = "n",
                       order = c("^`Absorption`$"),
                       # dep.var.labels.include = F,
                       dep.var.caption = "",
                       single.row = T)

cat(sub_interact_fun(table_s33), 
    file = "./stargazer_tables/table_s33.html")
```

```{r}
htmltools::includeHTML("./stargazer_tables/table_s33.html")
```

```{r}
rsq_table_fun(list(r06_spev, r07_spev, r08_spev, r06_dse, r07_dse, r08_dse))
```

### Table S34. Study 3, Spiritual Events: relationships with Absorption by country

```{r}
r09_us_spev <- lm(`Spiritual Events` ~ `Absorption`,
                  data = d3_abs_omit %>% 
                    filter(Country == "US") %>%
                    mutate_at(vars(`Spiritual Events`, `Absorption`),
                              ~ scale(.)))

r09_gh_spev <- lm(`Spiritual Events` ~ `Absorption`,
                  data = d3_abs_omit %>% 
                    filter(Country == "Ghana") %>%
                    mutate_at(vars(`Spiritual Events`, `Absorption`),
                              ~ scale(.)))

r09_th_spev <- lm(`Spiritual Events` ~ `Absorption`,
                  data = d3_abs_omit %>% 
                    filter(Country == "Thailand") %>%
                    mutate_at(vars(`Spiritual Events`, `Absorption`),
                              ~ scale(.)))

r09_ch_spev <- lm(`Spiritual Events` ~ `Absorption`,
                  data = d3_abs_omit %>% 
                    filter(Country == "China") %>%
                    mutate_at(vars(`Spiritual Events`, `Absorption`),
                              ~ scale(.)))

r09_vt_spev <- lm(`Spiritual Events` ~ `Absorption`,
                  data = d3_abs_omit %>% 
                    filter(Country == "Vanuatu") %>%
                    mutate_at(vars(`Spiritual Events`, `Absorption`),
                              ~ scale(.)))
```

```{r, include = F}
# run manually to check convergence
summary(r09_us_spev)
summary(r09_gh_spev)
summary(r09_th_spev)
summary(r09_ch_spev)
summary(r09_vt_spev)
```

```{r, results = "hide"}
table_s34 <- stargazer(r09_us_spev, r09_gh_spev, r09_th_spev, r09_ch_spev, r09_vt_spev, 
                       type = "html",
                       ci = T,
                       report = c("vcsp"),
                       digits = 2,
                       digits.extra = 1000,
                       omit.table.layout = "n",
                       order = c("^`Absorption`$"),
                       # dep.var.labels.include = F,
                       dep.var.caption = "",
                       single.row = T)

cat(sub_interact_fun(table_s34), 
    file = "./stargazer_tables/table_s34.html")
```

```{r}
htmltools::includeHTML("./stargazer_tables/table_s34.html")
```

### Table S35. Study 3, Daily Spiritual Experience: relationships with Absorption by country

```{r}
r09_us_dse <- lm(`Daily Spiritual Experiences` ~ `Absorption`,
                 data = d3_abs_omit %>% 
                   filter(Country == "US") %>%
                   mutate_at(vars(`Daily Spiritual Experiences`, `Absorption`),
                             ~ scale(.)))

r09_gh_dse <- lm(`Daily Spiritual Experiences` ~ `Absorption`,
                 data = d3_abs_omit %>% 
                   filter(Country == "Ghana") %>%
                   mutate_at(vars(`Daily Spiritual Experiences`, `Absorption`),
                             ~ scale(.)))

r09_th_dse <- lm(`Daily Spiritual Experiences` ~ `Absorption`,
                 data = d3_abs_omit %>% 
                   filter(Country == "Thailand") %>%
                   mutate_at(vars(`Daily Spiritual Experiences`, `Absorption`),
                             ~ scale(.)))

r09_ch_dse <- lm(`Daily Spiritual Experiences` ~ `Absorption`,
                 data = d3_abs_omit %>% 
                   filter(Country == "China") %>%
                   mutate_at(vars(`Daily Spiritual Experiences`, `Absorption`),
                             ~ scale(.)))

r09_vt_dse <- lm(`Daily Spiritual Experiences` ~ `Absorption`,
                 data = d3_abs_omit %>% 
                   filter(Country == "Vanuatu") %>%
                   mutate_at(vars(`Daily Spiritual Experiences`, `Absorption`),
                             ~ scale(.)))
```

```{r, include = F}
# run manually to check convergence
summary(r09_us_dse)
summary(r09_gh_dse)
summary(r09_th_dse)
summary(r09_ch_dse)
summary(r09_vt_dse)
```

```{r, results = "hide"}
table_s35 <- stargazer(r09_us_dse, r09_gh_dse, r09_th_dse, r09_ch_dse, r09_vt_dse, 
                       type = "html",
                       ci = T,
                       report = c("vcsp"),
                       digits = 2,
                       digits.extra = 1000,
                       omit.table.layout = "n",
                       order = c("^`Absorption`$"),
                       # dep.var.labels.include = F,
                       dep.var.caption = "",
                       single.row = T)

cat(sub_interact_fun(table_s35), 
    file = "./stargazer_tables/table_s35.html")
```

```{r}
htmltools::includeHTML("./stargazer_tables/table_s35.html")
```

## Study 4

## Relationships: Spiritual presence events

```{r}
d4_abs_omit <- d4_byq %>%
  select(p7_subj, p7_ctry, contains("_abs_")) %>%
  select(-ends_with("cat"), -ends_with("total"), -contains("_check")) %>%
  select(-p7_abs_sense.presc, -p7_abs_not.physical) %>%
  gather(question, response, -p7_subj, -p7_ctry) %>%
  group_by(p7_subj, p7_ctry) %>%
  summarise(Absorption = mean(response, na.rm = T)) %>%
  ungroup() %>%
  group_by(p7_ctry) %>%
  mutate(Absorption = scale(Absorption)) %>%
  ungroup() %>%
  distinct(p7_subj, Absorption) %>%
  rename(subject_id = p7_subj) %>%
  right_join(d4_fortab %>% select(-Absorption)) %>%
  filter(subject_id %in% d4_fortab$subject_id)
```

```{r}
# longform dataframe
d4_fortab_long_spirit <- d4_abs_omit %>% 
  gather(Scale, Score, 
         c(`Spiritual Events`, `Daily Spiritual Experience`)) %>%
  mutate(Scale = factor(Scale))
```

```{r}
# set contrasts
contrasts(d4_fortab_long_spirit$Country) <- cbind(": Gh." = c(-1, 1, 0, 0, 0),
                                                  ": Th." = c(-1, 0, 1, 0, 0),
                                                  ": Ch." = c(-1, 0, 0, 1, 0),
                                                  ": Va." = c(-1, 0, 0, 0, 1))
contrasts(d4_fortab_long_spirit$Gender) <- cbind(": male" = c(-1, 1))
contrasts(d4_fortab_long_spirit$Scale) <- cbind(": Spiritual Events" = c(-1, 1))
```


### Table S40. Study 4, measures of spiritual presence events: relationships with Porosity Vignettes and Absorption (sample as fixed effect)

```{r}
r03_spirit <- lmer(Score ~ Scale * `Porosity Vignettes`
                   + (1 | subject_id),
                   control = lmerControl(optimizer = "bobyqa"),
                   data = d4_fortab_long_spirit)

r04_spirit <- lmer(Score ~ Scale * `Porosity Scale`
                   + (1 | subject_id),
                   control = lmerControl(optimizer = "bobyqa"),
                   data = d4_fortab_long_spirit)

r05_spirit <- lmer(Score ~ Scale * Absorption
                   + (1 | subject_id),
                   control = lmerControl(optimizer = "bobyqa"),
                   data = d4_fortab_long_spirit)

r06_spirit <- lmer(Score ~ Scale * Country * `Porosity Vignettes`
                   + (1 | subject_id),
                   control = lmerControl(optimizer = "bobyqa"),
                   data = d4_fortab_long_spirit)

r07_spirit <- lmer(Score ~ Scale * Country * `Porosity Scale`
                   + (1 | subject_id),
                   control = lmerControl(optimizer = "bobyqa"),
                   data = d4_fortab_long_spirit)

r08_spirit <- lmer(Score ~ Scale * Country * Absorption
                   + (1 | subject_id),
                   control = lmerControl(optimizer = "bobyqa"),
                   data = d4_fortab_long_spirit)

r09_spirit <- lmer(Score ~ Scale * Country * `Porosity Vignettes` * Absorption
                   + (1 | subject_id),
                   control = lmerControl(optimizer = "bobyqa"),
                   data = d4_fortab_long_spirit)

r10_spirit <- lmer(Score ~ Scale * Country * `Porosity Scale` * Absorption
                   + (1 | subject_id),
                   control = lmerControl(optimizer = "bobyqa"),
                   data = d4_fortab_long_spirit)

r11_spirit <- lmer(Score ~ Scale * Country * `Porosity Vignettes` * Absorption
                   + Gender + Age
                   + (1 | subject_id),
                   control = lmerControl(optimizer = "bobyqa"),
                   data = d4_fortab_long_spirit)

r12_spirit <- lmer(Score ~ Scale * Country * `Porosity Scale` * Absorption
                   + Gender + Age
                   + (1 | subject_id),
                   control = lmerControl(optimizer = "bobyqa"),
                   data = d4_fortab_long_spirit)
```

```{r, include = F}
# run manually to check convergence
summary(r03_spirit)
summary(r04_spirit)
summary(r05_spirit)
summary(r06_spirit)
summary(r07_spirit)
summary(r08_spirit)
summary(r09_spirit)
summary(r10_spirit)
summary(r11_spirit)
summary(r12_spirit)
```

```{r, results = "hide"}
table_s40 <- stargazer(r03_spirit, #r04_spirit, 
                       r05_spirit, r06_spirit, #r07_spirit, 
                       r08_spirit, r09_spirit, #r10_spirit,
                       r11_spirit, #r12_spirit, 
                       type = "html",
                       ci = T,
                       report = c("vcsp"),
                       digits = 2,
                       digits.extra = 1000,
                       omit.table.layout = "n",
                       order = c("^`Porosity Vignettes`$",
                                 "^Absorption$",
                                 "^`Porosity Vignettes`:Absorption$"),
                       # dep.var.labels.include = F,
                       dep.var.caption = "",
                       single.row = T) 

cat(sub_interact_fun(table_s40), 
    file = "./stargazer_tables/table_s40.html")
```

```{r}
htmltools::includeHTML("./stargazer_tables/table_s40.html")
```

```{r}
rsq_table_fun(list(r03_spirit, #r04_spirit, 
                   r05_spirit, r06_spirit, #r07_spirit, 
                   r08_spirit, r09_spirit, #r10_spirit,
                   r11_spirit #, r12_spirit 
))
```

### Table S41. Study 4, measures of spiritual presence events: relationships with Porosity Scale and Absorption (sample as fixed effect)

```{r, results = "hide"}
table_s41 <- stargazer(#r03_spirit, 
  r04_spirit, r05_spirit, #r06_spirit,
  r07_spirit, r08_spirit, #r09_spirit, 
  r10_spirit, #r11_spirit, 
  r12_spirit, 
  type = "html",
  ci = T,
  report = c("vcsp"),
  digits = 2,
  digits.extra = 1000,
  omit.table.layout = "n",
  order = c("^`Porosity Scale`$",
            "^Absorption$",
            "^`Porosity Scale`:Absorption$"),
  # dep.var.labels.include = F,
  dep.var.caption = "",
  single.row = T) 

cat(sub_interact_fun(table_s41), 
    file = "./stargazer_tables/table_s41.html")
```

```{r}
htmltools::includeHTML("./stargazer_tables/table_s41.html")
```

```{r}
rsq_table_fun(list(#r03_spirit, 
  r04_spirit, r05_spirit, #r06_spirit,
  r07_spirit, r08_spirit, #r09_spirit, 
  r10_spirit, #r11_spirit, 
  r12_spirit))
```

### Table S42. Study 4, measures of spiritual presence events: relationships with Porosity Vignettes and Absorption (sample as random effect)

```{r}
r13_spirit <- lmer(Score ~ Scale
                   + (1 | Country/subject_id),
                   control = lmerControl(optimizer = "bobyqa"),
                   data = d4_fortab_long_spirit)

r14_spirit <- lmer(Score ~ Scale * `Porosity Vignettes`
                   + (1 | Country/subject_id),
                   # + (1 + `Porosity Vignettes` | Country), # adding to above, failed to converge
                   control = lmerControl(optimizer = "bobyqa"),
                   data = d4_fortab_long_spirit)

r15_spirit <- lmer(Score ~ Scale * `Porosity Scale`
                   + (1 | Country/subject_id),
                   # + (1 + `Porosity Scale` | Country), # adding to above, perfectly correlated ranfx
                   control = lmerControl(optimizer = "bobyqa"),
                   data = d4_fortab_long_spirit)

r16_spirit <- lmer(Score ~ Scale * Absorption
                   + (1 | Country/subject_id),
                   # + (1 + Absorption | Country), # adding to above, perfectly correlated ranfx
                   control = lmerControl(optimizer = "bobyqa"),
                   data = d4_fortab_long_spirit)

r17_spirit <- lmer(Score ~ Scale * `Porosity Vignettes` * Absorption
                   + (1 | Country/subject_id),
                   # + (1 + `Porosity Vignettes` * Absorption | Country), # adding to above, perfectly correlated ranfx
                   control = lmerControl(optimizer = "bobyqa"),
                   data = d4_fortab_long_spirit)

r18_spirit <- lmer(Score ~ Scale * `Porosity Scale` * Absorption
                   + (1 | Country/subject_id), 
                   # + (1 + `Porosity Scale` * Absorption | Country), # adding to above, perfectly correlated ranfx
                   control = lmerControl(optimizer = "bobyqa"),
                   data = d4_fortab_long_spirit)

r19_spirit <- lmer(Score ~ Scale * `Porosity Vignettes` * Absorption
                   + Gender + Age
                   + (1 | Country/subject_id),
                   # + (1 + `Porosity Vignettes` * Absorption + Gender + Age | Country),
                   control = lmerControl(optimizer = "bobyqa"),
                   data = d4_fortab_long_spirit)

r20_spirit <- lmer(Score ~ Scale * `Porosity Scale` * Absorption
                   + Gender + Age
                   + (1 | Country/subject_id),
                   # + (1 + `Porosity Scale` * Absorption + Gender + Age | Country),
                   control = lmerControl(optimizer = "bobyqa"),
                   data = d4_fortab_long_spirit)
```

```{r, include = F}
# run manually to check convergence
summary(r13_spirit)
summary(r14_spirit)
summary(r15_spirit)
summary(r16_spirit)
summary(r17_spirit)
summary(r18_spirit)
summary(r19_spirit)
summary(r20_spirit)
```

```{r, results = "hide"}
table_s42 <- stargazer(r13_spirit, r14_spirit, #r15_spirit,
                       r16_spirit, r17_spirit, #r18_spirit,
                       r19_spirit, #r20_spirit,
                       type = "html",
                       ci = T,
                       report = c("vcsp"),
                       digits = 2,
                       digits.extra = 1000,
                       omit.table.layout = "n",
                       order = c("^`Porosity Vignettes`$",
                                 "^Absorption$",
                                 "^`Porosity Vignettes`:Absorption$"),
                       # dep.var.labels.include = F,
                       dep.var.caption = "",
                       single.row = T)

cat(sub_interact_fun(table_s42), 
    file = "./stargazer_tables/table_s42.html")
```

```{r}
htmltools::includeHTML("./stargazer_tables/table_s42.html")
```

```{r}
rsq_table_fun(list(r13_spirit, r14_spirit, #r15_spirit,
                   r16_spirit, r17_spirit, #r18_spirit,
                   r19_spirit #, r20_spirit
))
```

### Table S43. Study 4, measures of spiritual presence events: relationships with Porosity Scale and Absorption (sample as random effect)

```{r, results = "hide"}
table_s43 <- stargazer(r13_spirit, #r14_spirit, 
                       r15_spirit, r16_spirit, #r17_spirit, 
                       r18_spirit, #r19_spirit, 
                       r20_spirit,
                       type = "html",
                       ci = T,
                       report = c("vcsp"),
                       digits = 2,
                       digits.extra = 1000,
                       omit.table.layout = "n",
                       order = c("^`Porosity Scale`$",
                                 "^Absorption$",
                                 "^`Porosity Scale`:Absorption$"),
                       # dep.var.labels.include = F,
                       dep.var.caption = "",
                       single.row = T)

cat(sub_interact_fun(table_s43), 
    file = "./stargazer_tables/table_s43.html")
```

```{r}
htmltools::includeHTML("./stargazer_tables/table_s43.html")
```

```{r}
rsq_table_fun(list(r13_spirit, #r14_spirit, 
                   r15_spirit, r16_spirit, #r17_spirit, 
                   r18_spirit, #r19_spirit 
                   r20_spirit
))
```

### Table S44. Study 4, measures of spiritual presence events: relationships with Porosity Vignettes by country

```{r}
r21_spirit_us <- lmer(Score ~ Scale * `Porosity Vignettes` 
                      + (1 | subject_id),
                      control = lmerControl(optimizer = "bobyqa"),
                      data = d4_fortab_long_spirit %>% filter(Country == "US") %>%
                        mutate_at(vars(Score, `Porosity Vignettes`), ~ scale(.)))

r21_spirit_gh <- lmer(Score ~ Scale * `Porosity Vignettes` 
                      + (1 | subject_id),
                      control = lmerControl(optimizer = "bobyqa"),
                      data = d4_fortab_long_spirit %>% filter(Country == "Ghana") %>%
                        mutate_at(vars(Score, ,`Porosity Vignettes`), ~ scale(.)))

r21_spirit_th <- lmer(Score ~ Scale * `Porosity Vignettes` 
                      + (1 | subject_id),
                      control = lmerControl(optimizer = "bobyqa"),
                      data = d4_fortab_long_spirit %>% filter(Country == "Thailand") %>%
                        mutate_at(vars(Score, `Porosity Vignettes`), ~ scale(.)))

r21_spirit_ch <- lmer(Score ~ Scale * `Porosity Vignettes` 
                      + (1 | subject_id),
                      control = lmerControl(optimizer = "bobyqa"),
                      data = d4_fortab_long_spirit %>% filter(Country == "China") %>%
                        mutate_at(vars(Score, `Porosity Vignettes`), ~ scale(.)))

r21_spirit_vt <- lmer(Score ~ Scale * `Porosity Vignettes` 
                      + (1 | subject_id),
                      control = lmerControl(optimizer = "bobyqa"),
                      data = d4_fortab_long_spirit %>% filter(Country == "Vanuatu") %>%
                        mutate_at(vars(Score, `Porosity Vignettes`), ~ scale(.)))
```

```{r, include = F}
# run manually to check convergence
summary(r21_spirit_us)
summary(r21_spirit_gh)
summary(r21_spirit_th)
summary(r21_spirit_ch)
summary(r21_spirit_vt)
```

```{r, results = "hide"}
table_s44 <- stargazer(r21_spirit_us, r21_spirit_gh, r21_spirit_th, 
                       r21_spirit_ch, r21_spirit_vt, 
                       type = "html",
                       ci = T,
                       report = c("vcsp"),
                       digits = 2,
                       digits.extra = 1000,
                       omit.table.layout = "n",
                       order = c("^`Porosity Vignettes`$"),
                       # dep.var.labels.include = F,
                       dep.var.caption = "",
                       single.row = T)

cat(sub_interact_fun(table_s44), 
    file = "./stargazer_tables/table_s44.html")
```

```{r}
htmltools::includeHTML("./stargazer_tables/table_s44.html")
```

```{r}
rsq_table_fun(list(r21_spirit_us, r21_spirit_gh, r21_spirit_th, 
                   r21_spirit_ch, r21_spirit_vt))
```

### Table S45. Study 4, measures of spiritual presence events: relationships with Porosity Scale by country

```{r}
r22_spirit_us <- lmer(Score ~ Scale * `Porosity Scale` 
                      + (1 | subject_id),
                      control = lmerControl(optimizer = "bobyqa"),
                      data = d4_fortab_long_spirit %>% filter(Country == "US") %>%
                        mutate_at(vars(Score, `Porosity Scale`), ~ scale(.)))

r22_spirit_gh <- lmer(Score ~ Scale * `Porosity Scale` 
                      + (1 | subject_id),
                      control = lmerControl(optimizer = "bobyqa"),
                      data = d4_fortab_long_spirit %>% filter(Country == "Ghana") %>%
                        mutate_at(vars(Score, `Porosity Scale`), ~ scale(.)))

r22_spirit_th <- lmer(Score ~ Scale * `Porosity Scale` 
                      + (1 | subject_id),
                      control = lmerControl(optimizer = "bobyqa"),
                      data = d4_fortab_long_spirit %>% filter(Country == "Thailand") %>%
                        mutate_at(vars(Score, `Porosity Scale`), ~ scale(.)))

r22_spirit_ch <- lmer(Score ~ Scale * `Porosity Scale` 
                      + (1 | subject_id),
                      control = lmerControl(optimizer = "bobyqa"),
                      data = d4_fortab_long_spirit %>% filter(Country == "China") %>%
                        mutate_at(vars(Score, `Porosity Scale`), ~ scale(.)))

r22_spirit_vt <- lmer(Score ~ Scale * `Porosity Scale` 
                      + (1 | subject_id),
                      control = lmerControl(optimizer = "bobyqa"),
                      data = d4_fortab_long_spirit %>% filter(Country == "Vanuatu") %>%
                        mutate_at(vars(Score, `Porosity Scale`), ~ scale(.)))
```

```{r, include = F}
# run manually to check convergence
summary(r22_spirit_us)
summary(r22_spirit_gh)
summary(r22_spirit_th)
summary(r22_spirit_ch)
summary(r22_spirit_vt)
```

```{r, results = "hide"}
table_s45 <- stargazer(r22_spirit_us, r22_spirit_gh, r22_spirit_th, 
                       r22_spirit_ch, r22_spirit_vt, 
                       type = "html",
                       ci = T,
                       report = c("vcsp"),
                       digits = 2,
                       digits.extra = 1000,
                       omit.table.layout = "n",
                       order = c("^`Porosity Scale`$"),
                       # dep.var.labels.include = F,
                       dep.var.caption = "",
                       single.row = T)

cat(sub_interact_fun(table_s45), 
    file = "./stargazer_tables/table_s45.html")
```

```{r}
htmltools::includeHTML("./stargazer_tables/table_s45.html")
```

```{r}
rsq_table_fun(list(r22_spirit_us, r22_spirit_gh, r22_spirit_th, 
                   r22_spirit_ch, r22_spirit_vt))
```

### Table S46. Study 4, measures of spiritual presence events: relationships with Absorption by country

```{r}
r23_spirit_us <- lmer(Score ~ Scale * `Absorption` 
                      + (1 | subject_id),
                      control = lmerControl(optimizer = "bobyqa"),
                      data = d4_fortab_long_spirit %>% filter(Country == "US") %>%
                        mutate_at(vars(Score, `Absorption`), ~ scale(.)))

r23_spirit_gh <- lmer(Score ~ Scale * `Absorption` 
                      + (1 | subject_id),
                      control = lmerControl(optimizer = "bobyqa"),
                      data = d4_fortab_long_spirit %>% filter(Country == "Ghana") %>%
                        mutate_at(vars(Score, `Absorption`), ~ scale(.)))

r23_spirit_th <- lmer(Score ~ Scale * `Absorption` 
                      + (1 | subject_id),
                      control = lmerControl(optimizer = "bobyqa"),
                      data = d4_fortab_long_spirit %>% filter(Country == "Thailand") %>%
                        mutate_at(vars(Score, `Absorption`), ~ scale(.)))

r23_spirit_ch <- lmer(Score ~ Scale * `Absorption` 
                      + (1 | subject_id),
                      control = lmerControl(optimizer = "bobyqa"),
                      data = d4_fortab_long_spirit %>% filter(Country == "China") %>%
                        mutate_at(vars(Score, `Absorption`), ~ scale(.)))

r23_spirit_vt <- lmer(Score ~ Scale * `Absorption` 
                      + (1 | subject_id),
                      control = lmerControl(optimizer = "bobyqa"),
                      data = d4_fortab_long_spirit %>% filter(Country == "Vanuatu") %>%
                        mutate_at(vars(Score, `Absorption`), ~ scale(.)))
```

```{r, include = F}
# run manually to check convergence
summary(r23_spirit_us)
summary(r23_spirit_gh)
summary(r23_spirit_th)
summary(r23_spirit_ch)
summary(r23_spirit_vt)
```

```{r, results = "hide"}
table_s46 <- stargazer(r23_spirit_us, r23_spirit_gh, r23_spirit_th, 
                       r23_spirit_ch, r23_spirit_vt, 
                       type = "html",
                       ci = T,
                       report = c("vcsp"),
                       digits = 2,
                       digits.extra = 1000,
                       omit.table.layout = "n",
                       order = c("^Absorption$"),
                       # dep.var.labels.include = F,
                       dep.var.caption = "",
                       single.row = T)

cat(sub_interact_fun(table_s46), 
    file = "./stargazer_tables/table_s46.html")
```

```{r}
htmltools::includeHTML("./stargazer_tables/table_s46.html")
```

```{r}
rsq_table_fun(list(r23_spirit_us, r23_spirit_gh, r23_spirit_th, 
                   r23_spirit_ch, r23_spirit_vt))
```

## Relationships: Secular anomalous events

### Setup

```{r}
# longform dataframe
d4_fortab_long_secular <- d4_abs_omit %>% 
  gather(Scale, Score, 
         c(Hallucinations, Paranormal)) %>%
  mutate(Scale = factor(Scale))
```

```{r}
# set contrasts
contrasts(d4_fortab_long_secular$Country) <- cbind(": Gh." = c(-1, 1, 0, 0, 0),
                                                   ": Th." = c(-1, 0, 1, 0, 0),
                                                   ": Ch." = c(-1, 0, 0, 1, 0),
                                                   ": Va." = c(-1, 0, 0, 0, 1))
contrasts(d4_fortab_long_secular$Gender) <- cbind(": male" = c(-1, 1))
contrasts(d4_fortab_long_secular$Scale) <- cbind(": Hallucinations" = c(1, -1))
```


### Table S47. Study 4, measures of secular anomalous events: relationships with Porosity Vignettes and Absorption (sample as fixed effect)

```{r}
r03_secular <- lmer(Score ~ Scale * `Porosity Vignettes`
                    + (1 | subject_id),
                    control = lmerControl(optimizer = "bobyqa"),
                    data = d4_fortab_long_secular)

r04_secular <- lmer(Score ~ Scale * `Porosity Scale`
                    + (1 | subject_id),
                    control = lmerControl(optimizer = "bobyqa"),
                    data = d4_fortab_long_secular)

r05_secular <- lmer(Score ~ Scale * Absorption
                    + (1 | subject_id),
                    control = lmerControl(optimizer = "bobyqa"),
                    data = d4_fortab_long_secular)

r06_secular <- lmer(Score ~ Scale * Country * `Porosity Vignettes`
                    + (1 | subject_id),
                    control = lmerControl(optimizer = "bobyqa"),
                    data = d4_fortab_long_secular)

r07_secular <- lmer(Score ~ Scale * Country * `Porosity Scale`
                    + (1 | subject_id),
                    control = lmerControl(optimizer = "bobyqa"),
                    data = d4_fortab_long_secular)

r08_secular <- lmer(Score ~ Scale * Country * Absorption
                    + (1 | subject_id),
                    control = lmerControl(optimizer = "bobyqa"),
                    data = d4_fortab_long_secular)

r09_secular <- lmer(Score ~ Scale * Country * `Porosity Vignettes` * Absorption
                    + (1 | subject_id),
                    control = lmerControl(optimizer = "bobyqa"),
                    data = d4_fortab_long_secular)

r10_secular <- lmer(Score ~ Scale * Country * `Porosity Scale` * Absorption
                    + (1 | subject_id),
                    control = lmerControl(optimizer = "bobyqa"),
                    data = d4_fortab_long_secular)

r11_secular <- lmer(Score ~ Scale * Country * `Porosity Vignettes` * Absorption
                    + Gender + Age
                    + (1 | subject_id),
                    control = lmerControl(optimizer = "bobyqa"),
                    data = d4_fortab_long_secular)

r12_secular <- lmer(Score ~ Scale * Country * `Porosity Scale` * Absorption
                    + Gender + Age
                    + (1 | subject_id),
                    control = lmerControl(optimizer = "bobyqa"),
                    data = d4_fortab_long_secular)
```

```{r, include = F}
# run manually to check convergence
summary(r03_secular)
summary(r04_secular)
summary(r05_secular)
summary(r06_secular)
summary(r07_secular)
summary(r08_secular)
summary(r09_secular)
summary(r10_secular)
summary(r11_secular)
summary(r12_secular)
```

```{r, results = "hide"}
table_s47 <- stargazer(r03_secular, #r04_secular, 
                       r05_secular, r06_secular, #r07_secular, 
                       r08_secular, r09_secular, #r10_secular,
                       r11_secular, #r12_secular, 
                       type = "html",
                       ci = T,
                       report = c("vcsp"),
                       digits = 2,
                       digits.extra = 1000,
                       omit.table.layout = "n",
                       order = c("^`Porosity Vignettes`$",
                                 "^Absorption$",
                                 "^`Porosity Vignettes`:Absorption$"),
                       # dep.var.labels.include = F,
                       dep.var.caption = "",
                       single.row = T) 

cat(sub_interact_fun(table_s47), 
    file = "./stargazer_tables/table_s47.html")
```

```{r}
htmltools::includeHTML("./stargazer_tables/table_s47.html")
```

```{r}
rsq_table_fun(list(r03_secular, #r04_secular, 
                   r05_secular, r06_secular, #r07_secular, 
                   r08_secular, r09_secular, #r10_secular,
                   r11_secular #, r12_secular 
))
```

### Table S48. Study 4, measures of secular anomalous events: relationships with Porosity Scale and Absorption (sample as fixed effect)

```{r, results = "hide"}
table_s48 <- stargazer(#r03_secular, 
  r04_secular, r05_secular, #r06_secular,
  r07_secular, r08_secular, #r09_secular, 
  r10_secular, #r11_secular, 
  r12_secular, 
  type = "html",
  ci = T,
  report = c("vcsp"),
  digits = 2,
  digits.extra = 1000,
  omit.table.layout = "n",
  order = c("^`Porosity Scale`$",
            "^Absorption$",
            "^`Porosity Scale`:Absorption$"),
  # dep.var.labels.include = F,
  dep.var.caption = "",
  single.row = T) 

cat(sub_interact_fun(table_s48), 
    file = "./stargazer_tables/table_s48.html")
```

```{r}
htmltools::includeHTML("./stargazer_tables/table_s48.html")
```

```{r}
rsq_table_fun(list(#r03_secular, 
  r04_secular, r05_secular, #r06_secular,
  r07_secular, r08_secular, #r09_secular, 
  r10_secular, #r11_secular, 
  r12_secular))
```

### Table S49. Study 4, measures of secular anomalous events: relationships with Porosity Vignettes and Absorption (sample as random effect)

```{r}
r13_secular <- lmer(Score ~ Scale
                    + (1 | Country/subject_id),
                    control = lmerControl(optimizer = "bobyqa"),
                    data = d4_fortab_long_secular)

r14_secular <- lmer(Score ~ Scale * `Porosity Vignettes`
                    + (1 | Country/subject_id),
                    # + (1 + `Porosity Vignettes` | Country), # adding to above, failed to converge
                    control = lmerControl(optimizer = "bobyqa"),
                    data = d4_fortab_long_secular)

r15_secular <- lmer(Score ~ Scale * `Porosity Scale`
                    + (1 | Country/subject_id),
                    # + (1 + `Porosity Scale` | Country), # adding to above, perfectly correlated ranfx
                    control = lmerControl(optimizer = "bobyqa"),
                    data = d4_fortab_long_secular)

r16_secular <- lmer(Score ~ Scale * Absorption
                    + (1 | Country/subject_id),
                    # + (1 + Absorption | Country), # adding to above, perfectly correlated ranfx
                    control = lmerControl(optimizer = "bobyqa"),
                    data = d4_fortab_long_secular)

r17_secular <- lmer(Score ~ Scale * `Porosity Vignettes` * Absorption
                    + (1 | Country/subject_id),
                    # + (1 + `Porosity Vignettes` * Absorption | Country), # adding to above, perfectly correlated ranfx
                    control = lmerControl(optimizer = "bobyqa"),
                    data = d4_fortab_long_secular)

r18_secular <- lmer(Score ~ Scale * `Porosity Scale` * Absorption
                    + (1 | Country/subject_id), 
                    # + (1 + `Porosity Scale` * Absorption | Country), # adding to above, perfectly correlated ranfx
                    control = lmerControl(optimizer = "bobyqa"),
                    data = d4_fortab_long_secular)

r19_secular <- lmer(Score ~ Scale * `Porosity Vignettes` * Absorption
                    + Gender + Age
                    + (1 | Country/subject_id),
                    # + (1 + `Porosity Vignettes` * Absorption + Gender + Age | Country),
                    control = lmerControl(optimizer = "bobyqa"),
                    data = d4_fortab_long_secular)

r20_secular <- lmer(Score ~ Scale * `Porosity Scale` * Absorption
                    + Gender + Age
                    + (1 | Country/subject_id),
                    # + (1 + `Porosity Scale` * Absorption + Gender + Age | Country),
                    control = lmerControl(optimizer = "bobyqa"),
                    data = d4_fortab_long_secular)
```

```{r, include = F}
# run manually to check convergence
summary(r13_secular)
summary(r14_secular)
summary(r15_secular)
summary(r16_secular)
summary(r17_secular)
summary(r18_secular)
summary(r19_secular)
summary(r20_secular)
```

```{r, results = "hide"}
table_s49 <- stargazer(r13_secular, r14_secular, #r15_secular,
                       r16_secular, r17_secular, #r18_secular,
                       r19_secular, #r20_secular,
                       type = "html",
                       ci = T,
                       report = c("vcsp"),
                       digits = 2,
                       digits.extra = 1000,
                       omit.table.layout = "n",
                       order = c("^`Porosity Vignettes`$",
                                 "^Absorption$",
                                 "^`Porosity Vignettes`:Absorption$"),
                       # dep.var.labels.include = F,
                       dep.var.caption = "",
                       single.row = T)

cat(sub_interact_fun(table_s49), 
    file = "./stargazer_tables/table_s49.html")
```

```{r}
htmltools::includeHTML("./stargazer_tables/table_s49.html")
```

```{r}
rsq_table_fun(list(r13_secular, r14_secular, #r15_secular,
                   r16_secular, r17_secular, #r18_secular,
                   r19_secular #, r20_secular
))
```

### Table S50. Study 4, measures of secular anomalous events: relationships with Porosity Scale and Absorption (sample as random effect)

```{r, results = "hide"}
table_s50 <- stargazer(r13_secular, #r14_secular, 
                       r15_secular, r16_secular, #r17_secular, 
                       r18_secular, #r19_secular, 
                       r20_secular,
                       type = "html",
                       ci = T,
                       report = c("vcsp"),
                       digits = 2,
                       digits.extra = 1000,
                       omit.table.layout = "n",
                       order = c("^`Porosity Scale`$",
                                 "^Absorption$",
                                 "^`Porosity Scale`:Absorption$"),
                       # dep.var.labels.include = F,
                       dep.var.caption = "",
                       single.row = T)

cat(sub_interact_fun(table_s50), 
    file = "./stargazer_tables/table_s50.html")
```

```{r}
htmltools::includeHTML("./stargazer_tables/table_s50.html")
```

```{r}
rsq_table_fun(list(r13_secular, #r14_secular, 
                   r15_secular, r16_secular, #r17_secular, 
                   r18_secular, #r19_secular 
                   r20_secular
))
```

### Table S51. Study 4, measures of secular anomalous events: relationships with Porosity Vignettes by country

```{r}
r21_secular_us <- lmer(Score ~ Scale * `Porosity Vignettes` 
                       + (1 | subject_id),
                       control = lmerControl(optimizer = "bobyqa"),
                       data = d4_fortab_long_secular %>% filter(Country == "US") %>%
                         mutate_at(vars(Score, `Porosity Vignettes`), ~ scale(.)))

r21_secular_gh <- lmer(Score ~ Scale * `Porosity Vignettes` 
                       + (1 | subject_id),
                       control = lmerControl(optimizer = "bobyqa"),
                       data = d4_fortab_long_secular %>% filter(Country == "Ghana") %>%
                         mutate_at(vars(Score, ,`Porosity Vignettes`), ~ scale(.)))

r21_secular_th <- lmer(Score ~ Scale * `Porosity Vignettes` 
                       + (1 | subject_id),
                       control = lmerControl(optimizer = "bobyqa"),
                       data = d4_fortab_long_secular %>% filter(Country == "Thailand") %>%
                         mutate_at(vars(Score, `Porosity Vignettes`), ~ scale(.)))

r21_secular_ch <- lmer(Score ~ Scale * `Porosity Vignettes` 
                       + (1 | subject_id),
                       control = lmerControl(optimizer = "bobyqa"),
                       data = d4_fortab_long_secular %>% filter(Country == "China") %>%
                         mutate_at(vars(Score, `Porosity Vignettes`), ~ scale(.)))

r21_secular_vt <- lmer(Score ~ Scale * `Porosity Vignettes` 
                       + (1 | subject_id),
                       control = lmerControl(optimizer = "bobyqa"),
                       data = d4_fortab_long_secular %>% filter(Country == "Vanuatu") %>%
                         mutate_at(vars(Score, `Porosity Vignettes`), ~ scale(.)))
```

```{r, include = F}
# run manually to check convergence
summary(r21_secular_us)
summary(r21_secular_gh)
summary(r21_secular_th)
summary(r21_secular_ch)
summary(r21_secular_vt)
```

```{r, results = "hide"}
table_s51 <- stargazer(r21_secular_us, r21_secular_gh, r21_secular_th, 
                       r21_secular_ch, r21_secular_vt, 
                       type = "html",
                       ci = T,
                       report = c("vcsp"),
                       digits = 2,
                       digits.extra = 1000,
                       omit.table.layout = "n",
                       order = c("^`Porosity Vignettes`$"),
                       # dep.var.labels.include = F,
                       dep.var.caption = "",
                       single.row = T)

cat(sub_interact_fun(table_s51), 
    file = "./stargazer_tables/table_s51.html")
```

```{r}
htmltools::includeHTML("./stargazer_tables/table_s51.html")
```

```{r}
rsq_table_fun(list(r21_secular_us, r21_secular_gh, r21_secular_th, 
                   r21_secular_ch, r21_secular_vt))
```

### Table S52. Study 4, measures of secular anomalous events: relationships with Porosity Scale by country

```{r}
r22_secular_us <- lmer(Score ~ Scale * `Porosity Scale` 
                       + (1 | subject_id),
                       control = lmerControl(optimizer = "bobyqa"),
                       data = d4_fortab_long_secular %>% filter(Country == "US") %>%
                         mutate_at(vars(Score, `Porosity Scale`), ~ scale(.)))

r22_secular_gh <- lmer(Score ~ Scale * `Porosity Scale` 
                       + (1 | subject_id),
                       control = lmerControl(optimizer = "bobyqa"),
                       data = d4_fortab_long_secular %>% filter(Country == "Ghana") %>%
                         mutate_at(vars(Score, `Porosity Scale`), ~ scale(.)))

r22_secular_th <- lmer(Score ~ Scale * `Porosity Scale` 
                       + (1 | subject_id),
                       control = lmerControl(optimizer = "bobyqa"),
                       data = d4_fortab_long_secular %>% filter(Country == "Thailand") %>%
                         mutate_at(vars(Score, `Porosity Scale`), ~ scale(.)))

r22_secular_ch <- lmer(Score ~ Scale * `Porosity Scale` 
                       + (1 | subject_id),
                       control = lmerControl(optimizer = "bobyqa"),
                       data = d4_fortab_long_secular %>% filter(Country == "China") %>%
                         mutate_at(vars(Score, `Porosity Scale`), ~ scale(.)))

r22_secular_vt <- lmer(Score ~ Scale * `Porosity Scale` 
                       + (1 | subject_id),
                       control = lmerControl(optimizer = "bobyqa"),
                       data = d4_fortab_long_secular %>% filter(Country == "Vanuatu") %>%
                         mutate_at(vars(Score, `Porosity Scale`), ~ scale(.)))
```

```{r, include = F}
# run manually to check convergence
summary(r22_secular_us)
summary(r22_secular_gh)
summary(r22_secular_th)
summary(r22_secular_ch)
summary(r22_secular_vt)
```

```{r, results = "hide"}
table_s52 <- stargazer(r22_secular_us, r22_secular_gh, r22_secular_th, 
                       r22_secular_ch, r22_secular_vt, 
                       type = "html",
                       ci = T,
                       report = c("vcsp"),
                       digits = 2,
                       digits.extra = 1000,
                       omit.table.layout = "n",
                       order = c("^`Porosity Scale`$"),
                       # dep.var.labels.include = F,
                       dep.var.caption = "",
                       single.row = T)

cat(sub_interact_fun(table_s52), 
    file = "./stargazer_tables/table_s52.html")
```

```{r}
htmltools::includeHTML("./stargazer_tables/table_s52.html")
```

```{r}
rsq_table_fun(list(r22_secular_us, r22_secular_gh, r22_secular_th, 
                   r22_secular_ch, r22_secular_vt))
```

### Table S53. Study 4, measures of secular anomalous events: relationships with Absorption by country

```{r}
r23_secular_us <- lmer(Score ~ Scale * `Absorption` 
                       + (1 | subject_id),
                       control = lmerControl(optimizer = "bobyqa"),
                       data = d4_fortab_long_secular %>% filter(Country == "US") %>%
                         mutate_at(vars(Score, `Absorption`), ~ scale(.)))

r23_secular_gh <- lmer(Score ~ Scale * `Absorption` 
                       + (1 | subject_id),
                       control = lmerControl(optimizer = "bobyqa"),
                       data = d4_fortab_long_secular %>% filter(Country == "Ghana") %>%
                         mutate_at(vars(Score, `Absorption`), ~ scale(.)))

r23_secular_th <- lmer(Score ~ Scale * `Absorption` 
                       + (1 | subject_id),
                       control = lmerControl(optimizer = "bobyqa"),
                       data = d4_fortab_long_secular %>% filter(Country == "Thailand") %>%
                         mutate_at(vars(Score, `Absorption`), ~ scale(.)))

r23_secular_ch <- lmer(Score ~ Scale * `Absorption` 
                       + (1 | subject_id),
                       control = lmerControl(optimizer = "bobyqa"),
                       data = d4_fortab_long_secular %>% filter(Country == "China") %>%
                         mutate_at(vars(Score, `Absorption`), ~ scale(.)))

r23_secular_vt <- lmer(Score ~ Scale * `Absorption` 
                       + (1 | subject_id),
                       control = lmerControl(optimizer = "bobyqa"),
                       data = d4_fortab_long_secular %>% filter(Country == "Vanuatu") %>%
                         mutate_at(vars(Score, `Absorption`), ~ scale(.)))
```

```{r, include = F}
# run manually to check convergence
summary(r23_secular_us)
summary(r23_secular_gh)
summary(r23_secular_th)
summary(r23_secular_ch)
summary(r23_secular_vt)
```

```{r, results = "hide"}
table_s53 <- stargazer(r23_secular_us, r23_secular_gh, r23_secular_th, 
                       r23_secular_ch, r23_secular_vt, 
                       type = "html",
                       ci = T,
                       report = c("vcsp"),
                       digits = 2,
                       digits.extra = 1000,
                       omit.table.layout = "n",
                       order = c("^Absorption$"),
                       # dep.var.labels.include = F,
                       dep.var.caption = "",
                       single.row = T)

cat(sub_interact_fun(table_s53), 
    file = "./stargazer_tables/table_s53.html")
```

```{r}
htmltools::includeHTML("./stargazer_tables/table_s53.html")
```

```{r}
rsq_table_fun(list(r23_secular_us, r23_secular_gh, r23_secular_th, 
                   r23_secular_ch, r23_secular_vt))
```


# Exploratory factor analyses

## Study 1

### All sites

```{r}
d1_abs <- d1_byq %>%
  select(subject_id, ends_with("_abs")) %>%
  column_to_rownames("subject_id")
```

```{r}
fa.parallel(d1_abs)
```

```{r}
d1_all_efa <- fa(d1_abs, nfactors = 4, rotate = "oblimin")
```

```{r}
d1_all_loadings <- d1_all_efa$loadings[] %>% 
  fa.sort() %>%
  data.frame() %>%
  rownames_to_column("question") %>%
  mutate(order = 1:nrow(.)) %>%
  gather(factor, loading, -question, -order)
```

```{r, fig.width = 3, fig.asp = 1}
d1_all_loadings %>%
  mutate(question = gsub("_abs", "", question)) %>%
  ggplot(aes(x = factor, y = reorder(question, desc(order)), fill = loading)) +
  geom_tile(color = "black") +
  geom_text(aes(label = format(round(loading, 2), nsmall = 2)), size = 3) +
  scale_fill_distiller(palette = "RdYlBu", limits = c(-1, 1)) +
  labs(title = "Study 1: All sites", x = "factor", y = "question")
```

### US

```{r}
d1_abs_us <- d1_byq %>%
  filter(country == "US") %>%
  select(subject_id, ends_with("_abs")) %>%
  column_to_rownames("subject_id")
```

```{r}
fa.parallel(d1_abs_us)
```

```{r}
d1_us_efa <- fa(d1_abs, nfactors = 3, rotate = "oblimin")
```

```{r}
d1_us_loadings <- d1_us_efa$loadings[] %>% 
  fa.sort() %>%
  data.frame() %>%
  rownames_to_column("question") %>%
  mutate(order = 1:nrow(.)) %>%
  gather(factor, loading, -question, -order)
```

```{r, fig.width = 3, fig.asp = 1}
d1_us_loadings %>%
  mutate(question = gsub("_abs", "", question)) %>%
  ggplot(aes(x = factor, y = reorder(question, desc(order)), fill = loading)) +
  geom_tile(color = "black") +
  geom_text(aes(label = format(round(loading, 2), nsmall = 2)), size = 3) +
  scale_fill_distiller(palette = "RdYlBu", limits = c(-1, 1)) +
  labs(title = "Study 1: US", x = "factor", y = "question")
```

### Ghana

```{r}
d1_abs_gh <- d1_byq %>%
  filter(country == "Ghana") %>%
  select(subject_id, ends_with("_abs")) %>%
  column_to_rownames("subject_id")
```

```{r}
fa.parallel(d1_abs_gh)
```

```{r}
d1_gh_efa <- fa(d1_abs, nfactors = 2, rotate = "oblimin")
```

```{r}
d1_gh_loadings <- d1_gh_efa$loadings[] %>% 
  fa.sort() %>%
  data.frame() %>%
  rownames_to_column("question") %>%
  mutate(order = 1:nrow(.)) %>%
  gather(factor, loading, -question, -order)
```

```{r, fig.width = 3, fig.asp = 1}
d1_gh_loadings %>%
  mutate(question = gsub("_abs", "", question)) %>%
  ggplot(aes(x = factor, y = reorder(question, desc(order)), fill = loading)) +
  geom_tile(color = "black") +
  geom_text(aes(label = format(round(loading, 2), nsmall = 2)), size = 3) +
  scale_fill_distiller(palette = "RdYlBu", limits = c(-1, 1)) +
  labs(title = "Study 1: Ghana", x = "factor", y = "question")
```

### Thailand

```{r}
d1_abs_th <- d1_byq %>%
  filter(country == "Thailand") %>%
  select(subject_id, ends_with("_abs")) %>%
  column_to_rownames("subject_id")
```

```{r}
fa.parallel(d1_abs_th)
```

```{r}
d1_th_efa <- fa(d1_abs, nfactors = 1, rotate = "oblimin")
```

```{r}
d1_th_loadings <- d1_th_efa$loadings[] %>% 
  fa.sort() %>%
  data.frame() %>%
  rownames_to_column("question") %>%
  mutate(order = 1:nrow(.)) %>%
  gather(factor, loading, -question, -order)
```

```{r, fig.width = 3, fig.asp = 1}
d1_th_loadings %>%
  mutate(question = gsub("_abs", "", question)) %>%
  ggplot(aes(x = factor, y = reorder(question, desc(order)), fill = loading)) +
  geom_tile(color = "black") +
  geom_text(aes(label = format(round(loading, 2), nsmall = 2)), size = 3) +
  scale_fill_distiller(palette = "RdYlBu", limits = c(-1, 1)) +
  labs(title = "Study 1: Thailand", x = "factor", y = "question")
```

### China

```{r}
d1_abs_ch <- d1_byq %>%
  filter(country == "China") %>%
  select(subject_id, ends_with("_abs")) %>%
  column_to_rownames("subject_id")
```

```{r}
fa.parallel(d1_abs_ch)
```

```{r}
d1_ch_efa <- fa(d1_abs, nfactors = 1, rotate = "oblimin")
```

```{r}
d1_ch_loadings <- d1_ch_efa$loadings[] %>% 
  fa.sort() %>%
  data.frame() %>%
  rownames_to_column("question") %>%
  mutate(order = 1:nrow(.)) %>%
  gather(factor, loading, -question, -order)
```

```{r, fig.width = 3, fig.asp = 1}
d1_ch_loadings %>%
  mutate(question = gsub("_abs", "", question)) %>%
  ggplot(aes(x = factor, y = reorder(question, desc(order)), fill = loading)) +
  geom_tile(color = "black") +
  geom_text(aes(label = format(round(loading, 2), nsmall = 2)), size = 3) +
  scale_fill_distiller(palette = "RdYlBu", limits = c(-1, 1)) +
  labs(title = "Study 1: China", x = "factor", y = "question")
```

### Vanuatu

```{r}
d1_abs_vt <- d1_byq %>%
  filter(country == "Vanuatu") %>%
  select(subject_id, ends_with("_abs")) %>%
  column_to_rownames("subject_id")
```

```{r}
fa.parallel(d1_abs_vt)
```

```{r}
d1_vt_efa <- fa(d1_abs, nfactors = 5, rotate = "oblimin")
```

```{r}
d1_vt_loadings <- d1_vt_efa$loadings[] %>% 
  fa.sort() %>%
  data.frame() %>%
  rownames_to_column("question") %>%
  mutate(order = 1:nrow(.)) %>%
  gather(factor, loading, -question, -order)
```

```{r, fig.width = 3, fig.asp = 1}
d1_vt_loadings %>%
  mutate(question = gsub("_abs", "", question)) %>%
  ggplot(aes(x = factor, y = reorder(question, desc(order)), fill = loading)) +
  geom_tile(color = "black") +
  geom_text(aes(label = format(round(loading, 2), nsmall = 2)), size = 3) +
  scale_fill_distiller(palette = "RdYlBu", limits = c(-1, 1)) +
  labs(title = "Study 1: Vanuatu", x = "factor", y = "question")
```

## Study 3

### All sites

```{r}
d3_abs <- d3_byq %>%
  select(subj, question, response) %>%
  filter(question %in% c("exwl_01", "exwl_02", "exwl_03", "exwl_04", "exwl_05", "exwl_06",
                         "exwl_07", "exwl_08", "exwl_09", "exwl_10", "exwl_11", "exwl_12",
                         "exwl_13", "exwl_14", "exwl_15", "exwl_16", "exwl_17", "exwl_18",
                         "exwl_19", "exwl_20", "exwl_21", "exwl_22", "exwl_23", "exwl_24",
                         "exwl_25", "exwl_26", "exwl_27", "exwl_28", "exwl_29", "exwl_30",
                         "exwl_31", "exwl_32", "exwl_33", "exwl_34")) %>%
  mutate(question = factor(
    question,
    levels = c("exwl_01", "exwl_02", "exwl_03", "exwl_04", 
               "exwl_05", "exwl_06", "exwl_07", "exwl_08", 
               "exwl_09", "exwl_10", "exwl_11", "exwl_12",
               "exwl_13", "exwl_14", "exwl_15", "exwl_16", 
               "exwl_17", "exwl_18", "exwl_19", "exwl_20", 
               "exwl_21", "exwl_22", "exwl_23", "exwl_24",
               "exwl_25", "exwl_26", "exwl_27", "exwl_28", 
               "exwl_29", "exwl_30", "exwl_31", "exwl_32", 
               "exwl_33", "exwl_34"),
    labels = c("childexp", "poeticlanguage", "forgetsurrounding", "starepicture", 
               "mindenvelopworld", "cloudshapes", "vividdaydream", "mysticalexp", 
               "stepoutsidemyself", "textures", "doublyreal", "caughtupinmusic", 
               "heavybody", "sensepresence", "woodfireimagine", "immersednature", 
               "colormeaning", "wanderthoughtstask", "clearpastexp", "meaninglesstoothers", 
               "actinginplay", "visualthoughts", "delightsmallthings", "organmusic", 
               "changenoise", "vividsmellmemories", "synesthesiasound", "predictwords", 
               "physicalmemories", "fascinatingvoice", "invisiblepresence", "sponthoughtsimages", 
               "synesthesiasmell", "emosunset"))) %>%
  spread(question, response) %>%
  column_to_rownames("subj")
```

```{r}
fa.parallel(d3_abs)
```

```{r}
d3_all_efa <- fa(d3_abs, nfactors = 6, rotate = "oblimin")
```

```{r}
d3_all_loadings <- d3_all_efa$loadings[] %>% 
  fa.sort() %>%
  data.frame() %>%
  rownames_to_column("question") %>%
  mutate(order = 1:nrow(.)) %>%
  gather(factor, loading, -question, -order)
```

```{r, fig.width = 3, fig.asp = 1}
d3_all_loadings %>%
  mutate(question = gsub("_abs", "", question)) %>%
  ggplot(aes(x = factor, y = reorder(question, desc(order)), fill = loading)) +
  geom_tile(color = "black") +
  geom_text(aes(label = format(round(loading, 2), nsmall = 2)), size = 3) +
  scale_fill_distiller(palette = "RdYlBu", limits = c(-1, 1)) +
  labs(title = "Study 3: All sites", x = "factor", y = "question")
```

### US

```{r}
d3_abs_us <- d3_abs %>%
  rownames_to_column("subj") %>%
  filter(subj %in% c(d3_byq %>% filter(ctry == "us") %>% distinct(subj) %>% pull(subj))) %>%
  column_to_rownames("subj")
```

```{r}
fa.parallel(d3_abs_us)
```

```{r}
d3_us_efa <- fa(d3_abs, nfactors = 4, rotate = "oblimin")
```

```{r}
d3_us_loadings <- d3_us_efa$loadings[] %>% 
  fa.sort() %>%
  data.frame() %>%
  rownames_to_column("question") %>%
  mutate(order = 1:nrow(.)) %>%
  gather(factor, loading, -question, -order)
```

```{r, fig.width = 3, fig.asp = 1}
d3_us_loadings %>%
  mutate(question = gsub("_abs", "", question)) %>%
  ggplot(aes(x = factor, y = reorder(question, desc(order)), fill = loading)) +
  geom_tile(color = "black") +
  geom_text(aes(label = format(round(loading, 2), nsmall = 2)), size = 3) +
  scale_fill_distiller(palette = "RdYlBu", limits = c(-1, 1)) +
  labs(title = "Study 3: US", x = "factor", y = "question")
```

### Ghana

```{r}
d3_abs_gh <- d3_abs %>%
  rownames_to_column("subj") %>%
  filter(subj %in% c(d3_byq %>% filter(ctry == "ghana") %>% distinct(subj) %>% pull(subj))) %>%
  column_to_rownames("subj")
```

```{r}
fa.parallel(d3_abs_gh)
```

```{r}
d3_gh_efa <- fa(d3_abs, nfactors = 2, rotate = "oblimin")
```

```{r}
d3_gh_loadings <- d3_gh_efa$loadings[] %>% 
  fa.sort() %>%
  data.frame() %>%
  rownames_to_column("question") %>%
  mutate(order = 1:nrow(.)) %>%
  gather(factor, loading, -question, -order)
```

```{r, fig.width = 3, fig.asp = 1}
d3_gh_loadings %>%
  mutate(question = gsub("_abs", "", question)) %>%
  ggplot(aes(x = factor, y = reorder(question, desc(order)), fill = loading)) +
  geom_tile(color = "black") +
  geom_text(aes(label = format(round(loading, 2), nsmall = 2)), size = 3) +
  scale_fill_distiller(palette = "RdYlBu", limits = c(-1, 1)) +
  labs(title = "Study 3: Ghana", x = "factor", y = "question")
```

### Thailand

```{r}
d3_abs_th <- d3_abs %>%
  rownames_to_column("subj") %>%
  filter(subj %in% c(d3_byq %>% filter(ctry == "thailand") %>% distinct(subj) %>% pull(subj))) %>%
  column_to_rownames("subj")
```

```{r}
fa.parallel(d3_abs_th)
```

```{r}
d3_th_efa <- fa(d3_abs, nfactors = 1, rotate = "oblimin")
```

```{r}
d3_th_loadings <- d3_th_efa$loadings[] %>% 
  fa.sort() %>%
  data.frame() %>%
  rownames_to_column("question") %>%
  mutate(order = 1:nrow(.)) %>%
  gather(factor, loading, -question, -order)
```

```{r, fig.width = 3, fig.asp = 1}
d3_th_loadings %>%
  mutate(question = gsub("_abs", "", question)) %>%
  ggplot(aes(x = factor, y = reorder(question, desc(order)), fill = loading)) +
  geom_tile(color = "black") +
  geom_text(aes(label = format(round(loading, 2), nsmall = 2)), size = 3) +
  scale_fill_distiller(palette = "RdYlBu", limits = c(-1, 1)) +
  labs(title = "Study 3: Thailand", x = "factor", y = "question")
```

### China

```{r}
d3_abs_ch <- d3_abs %>%
  rownames_to_column("subj") %>%
  filter(subj %in% c(d3_byq %>% filter(ctry == "china") %>% distinct(subj) %>% pull(subj))) %>%
  column_to_rownames("subj")
```

```{r}
fa.parallel(d3_abs_ch)
```

```{r}
d3_ch_efa <- fa(d3_abs, nfactors = 2, rotate = "oblimin")
```

```{r}
d3_ch_loadings <- d3_ch_efa$loadings[] %>% 
  fa.sort() %>%
  data.frame() %>%
  rownames_to_column("question") %>%
  mutate(order = 1:nrow(.)) %>%
  gather(factor, loading, -question, -order)
```

```{r, fig.width = 3, fig.asp = 1}
d3_ch_loadings %>%
  mutate(question = gsub("_abs", "", question)) %>%
  ggplot(aes(x = factor, y = reorder(question, desc(order)), fill = loading)) +
  geom_tile(color = "black") +
  geom_text(aes(label = format(round(loading, 2), nsmall = 2)), size = 3) +
  scale_fill_distiller(palette = "RdYlBu", limits = c(-1, 1)) +
  labs(title = "Study 3: China", x = "factor", y = "question")
```

### Vanuatu

```{r}
d3_abs_vt <- d3_abs %>%
  rownames_to_column("subj") %>%
  filter(subj %in% c(d3_byq %>% filter(ctry == "vanuatu") %>% distinct(subj) %>% pull(subj))) %>%
  column_to_rownames("subj")
```

```{r}
fa.parallel(d3_abs_vt)
```

```{r}
d3_vt_efa <- fa(d3_abs, nfactors = 2, rotate = "oblimin")
```

```{r}
d3_vt_loadings <- d3_vt_efa$loadings[] %>% 
  fa.sort() %>%
  data.frame() %>%
  rownames_to_column("question") %>%
  mutate(order = 1:nrow(.)) %>%
  gather(factor, loading, -question, -order)
```

```{r, fig.width = 3, fig.asp = 1}
d3_vt_loadings %>%
  mutate(question = gsub("_abs", "", question)) %>%
  ggplot(aes(x = factor, y = reorder(question, desc(order)), fill = loading)) +
  geom_tile(color = "black") +
  geom_text(aes(label = format(round(loading, 2), nsmall = 2)), size = 3) +
  scale_fill_distiller(palette = "RdYlBu", limits = c(-1, 1)) +
  labs(title = "Study 3: Vanuatu", x = "factor", y = "question")
```

## Study 4

### All sites

```{r}
d4_abs <- d4_byq %>%
  select(p7_subj, contains("_abs_")) %>%
  select(-ends_with("cat"), -ends_with("total"), -contains("_check")) %>%
  column_to_rownames("p7_subj")
```

```{r}
fa.parallel(d4_abs)
```

```{r}
d4_all_efa <- fa(d4_abs, nfactors = 4, rotate = "oblimin")
```

```{r}
d4_all_loadings <- d4_all_efa$loadings[] %>% 
  fa.sort() %>%
  data.frame() %>%
  rownames_to_column("question") %>%
  mutate(order = 1:nrow(.)) %>%
  gather(factor, loading, -question, -order)
```

```{r, fig.width = 3, fig.asp = 1}
d4_all_loadings %>%
  mutate(question = gsub("_abs", "", question),
         question = gsub("p7_", "", question)) %>%
  ggplot(aes(x = factor, y = reorder(question, desc(order)), fill = loading)) +
  geom_tile(color = "black") +
  geom_text(aes(label = format(round(loading, 2), nsmall = 2)), size = 3) +
  scale_fill_distiller(palette = "RdYlBu", limits = c(-1, 1)) +
  labs(title = "Study 4: All sites", x = "factor", y = "question")
```

### US

```{r}
d4_abs_us <- d4_abs %>%
  rownames_to_column("p7_subj") %>%
  filter(p7_subj %in% c(d4_byq %>% 
                          filter(p7_ctry == "US") %>% 
                          distinct(p7_subj) %>% 
                          pull(p7_subj))) %>%
  column_to_rownames("p7_subj")
```

```{r}
fa.parallel(d4_abs_us)
```

```{r}
d4_us_efa <- fa(d4_abs_us, nfactors = 2, rotate = "oblimin")
```

```{r}
d4_us_loadings <- d4_us_efa$loadings[] %>% 
  fa.sort() %>%
  data.frame() %>%
  rownames_to_column("question") %>%
  mutate(order = 1:nrow(.)) %>%
  gather(factor, loading, -question, -order)
```

```{r, fig.width = 3, fig.asp = 1}
d4_us_loadings %>%
  mutate(question = gsub("_abs", "", question),
         question = gsub("p7_", "", question)) %>%
  ggplot(aes(x = factor, y = reorder(question, desc(order)), fill = loading)) +
  geom_tile(color = "black") +
  geom_text(aes(label = format(round(loading, 2), nsmall = 2)), size = 3) +
  scale_fill_distiller(palette = "RdYlBu", limits = c(-1, 1)) +
  labs(title = "Study 4: US", x = "factor", y = "question")
```

### Ghana

```{r}
d4_abs_gh <- d4_abs %>%
  rownames_to_column("p7_subj") %>%
  filter(p7_subj %in% c(d4_byq %>% 
                          filter(p7_ctry == "Ghana") %>% 
                          distinct(p7_subj) %>% 
                          pull(p7_subj))) %>%
  column_to_rownames("p7_subj")
```

```{r}
fa.parallel(d4_abs_gh)
```

```{r}
d4_gh_efa <- fa(d4_abs_gh, nfactors = 4, rotate = "oblimin")
```

```{r}
d4_gh_loadings <- d4_gh_efa$loadings[] %>% 
  fa.sort() %>%
  data.frame() %>%
  rownames_to_column("question") %>%
  mutate(order = 1:nrow(.)) %>%
  gather(factor, loading, -question, -order)
```

```{r, fig.width = 3, fig.asp = 1}
d4_gh_loadings %>%
  mutate(question = gsub("_abs", "", question),
         question = gsub("p7_", "", question)) %>%
  ggplot(aes(x = factor, y = reorder(question, desc(order)), fill = loading)) +
  geom_tile(color = "black") +
  geom_text(aes(label = format(round(loading, 2), nsmall = 2)), size = 3) +
  scale_fill_distiller(palette = "RdYlBu", limits = c(-1, 1)) +
  labs(title = "Study 4: Ghana", x = "factor", y = "question")
```

### Thailand

```{r}
d4_abs_th <- d4_abs %>%
  rownames_to_column("p7_subj") %>%
  filter(p7_subj %in% c(d4_byq %>% 
                          filter(p7_ctry == "Thailand") %>% 
                          distinct(p7_subj) %>% 
                          pull(p7_subj))) %>%
  column_to_rownames("p7_subj")
```

```{r}
fa.parallel(d4_abs_th)
```

```{r}
d4_th_efa <- fa(d4_abs_th, nfactors = 1, rotate = "oblimin")
```

```{r}
d4_th_loadings <- d4_th_efa$loadings[] %>% 
  fa.sort() %>%
  data.frame() %>%
  rownames_to_column("question") %>%
  mutate(order = 1:nrow(.)) %>%
  gather(factor, loading, -question, -order)
```

```{r, fig.width = 3, fig.asp = 1}
d4_th_loadings %>%
  mutate(question = gsub("_abs", "", question)) %>%
  ggplot(aes(x = factor, y = reorder(question, desc(order)), fill = loading)) +
  geom_tile(color = "black") +
  geom_text(aes(label = format(round(loading, 2), nsmall = 2)), size = 3) +
  scale_fill_distiller(palette = "RdYlBu", limits = c(-1, 1)) +
  labs(title = "Study 4: Thailand", x = "factor", y = "question")
```

### China

```{r}
d4_abs_ch <- d4_abs %>%
  rownames_to_column("p7_subj") %>%
  filter(p7_subj %in% c(d4_byq %>% 
                          filter(p7_ctry == "China") %>% 
                          distinct(p7_subj) %>% 
                          pull(p7_subj))) %>%
  column_to_rownames("p7_subj")
```

```{r}
fa.parallel(d4_abs_ch)
```

```{r}
d4_ch_efa <- fa(d4_abs_ch, nfactors = 1, rotate = "oblimin")
```

```{r}
d4_ch_loadings <- d4_ch_efa$loadings[] %>% 
  fa.sort() %>%
  data.frame() %>%
  rownames_to_column("question") %>%
  mutate(order = 1:nrow(.)) %>%
  gather(factor, loading, -question, -order)
```

```{r, fig.width = 3, fig.asp = 1}
d4_ch_loadings %>%
  mutate(question = gsub("_abs", "", question),
         question = gsub("p7_", "", question)) %>%
  ggplot(aes(x = factor, y = reorder(question, desc(order)), fill = loading)) +
  geom_tile(color = "black") +
  geom_text(aes(label = format(round(loading, 2), nsmall = 2)), size = 3) +
  scale_fill_distiller(palette = "RdYlBu", limits = c(-1, 1)) +
  labs(title = "Study 4: China", x = "factor", y = "question")
```

### Vanuatu

```{r}
d4_abs_vt <- d4_abs %>%
  rownames_to_column("p7_subj") %>%
  filter(p7_subj %in% c(d4_byq %>% 
                          filter(p7_ctry == "Vanuatu") %>% 
                          distinct(p7_subj) %>% 
                          pull(p7_subj))) %>%
  column_to_rownames("p7_subj")
```

```{r}
fa.parallel(d4_abs_vt)
```

```{r}
d4_vt_efa <- fa(d4_abs_vt, nfactors = 6, rotate = "oblimin")
```

```{r}
d4_vt_loadings <- d4_vt_efa$loadings[] %>% 
  fa.sort() %>%
  data.frame() %>%
  rownames_to_column("question") %>%
  mutate(order = 1:nrow(.)) %>%
  gather(factor, loading, -question, -order)
```

```{r, fig.width = 3, fig.asp = 1}
d4_vt_loadings %>%
  mutate(question = gsub("_abs", "", question),
         question = gsub("p7_", "", question)) %>%
  ggplot(aes(x = factor, y = reorder(question, desc(order)), fill = loading)) +
  geom_tile(color = "black") +
  geom_text(aes(label = format(round(loading, 2), nsmall = 2)), size = 3) +
  scale_fill_distiller(palette = "RdYlBu", limits = c(-1, 1)) +
  labs(title = "Study 4: Vanuatu", x = "factor", y = "question")
```

## All studies

```{r}
d_all_abs <- d1_abs %>% 
  rename_all(~gsub("_abs", "", .)) %>%
  rownames_to_column("subject_id") %>%
  mutate(study = "s1") %>%
  gather(question, response, -c(study, subject_id)) %>%
  full_join(d3_abs %>%
              rownames_to_column("subject_id") %>%
              mutate(study = "s3") %>%
              gather(question, response, -c(study, subject_id))) %>%
  full_join(d4_abs %>%
              rownames_to_column("subject_id") %>%
              mutate(study = "s4") %>%
              gather(question, response, -c(study, subject_id)) %>%
              mutate(question = recode(question,
                                       "p7_abs_child.exp" = "childexp",
                                       "p7_abs_poetic" = "poeticlanguage", 
                                       "p7_abs_tv.real" = "forgetsurrounding",
                                       "p7_abs_see.image" = "starepicture",
                                       "p7_abs_mind.world" = "mindenvelopworld", 
                                       "p7_abs_clouds" = "cloudshapes", 
                                       "p7_abs_vivid.dreams" = "vividdaydream",
                                       "p7_abs_mystic.exp" = "mysticalexp", 
                                       "p7_abs_step.outside" = "stepoutsidemyself", 
                                       "p7_abs_textures" = "textures", 
                                       "p7_abs_too.real" = "doublyreal", 
                                       "p7_abs_music.attn" = "caughtupinmusic", 
                                       "p7_abs_heavy.body" = "heavybody",
                                       "p7_abs_sense.presc" = "sensepresence",
                                       "p7_abs_fire" = "woodfireimagine", 
                                       "p7_abs_nature.art" = "immersednature", 
                                       "p7_abs_colors" = "colormeaning", 
                                       "p7_abs_thght.wander" = "wanderthoughtstask", 
                                       "p7_abs_vivid.past" = "clearpastexp", 
                                       "p7_abs_makes.sense" = "meaninglesstoothers", 
                                       "p7_abs_become.chctr" = "actinginplay", 
                                       "p7_abs_visual.thghts" = "visualthoughts", 
                                       "p7_abs_small.things" = "delightsmallthings", 
                                       "p7_abs_music.lift" = "organmusic", 
                                       "p7_abs_noise.music" = "changenoise", 
                                       "p7_abs_scented.mem" = "vividsmellmemories", 
                                       "p7_abs_visual.music" = "synesthesiasound", 
                                       "p7_abs_before.said" = "predictwords", 
                                       "p7_abs_physical.mem" = "physicalmemories", 
                                       "p7_abs_voice.sound" = "fascinatingvoice", 
                                       "p7_abs_not.physical" = "invisiblepresence",
                                       "p7_abs_thgts.image" = "sponthoughtsimages", 
                                       "p7_abs_odor.to.color" = "synesthesiasmell",
                                       "p7_abs_sunset" = "emosunset"))) %>%
  unite("study_sub", c(study, subject_id)) %>%
  spread(question, response) %>%
  column_to_rownames("study_sub")
```

```{r}
fa.parallel(d_all_abs)
```

```{r}
d_all_all_efa <- fa(d_all_abs, nfactors = 6, rotate = "oblimin")
```

```{r}
d_all_all_loadings <- d_all_all_efa$loadings[] %>% 
  fa.sort() %>%
  data.frame() %>%
  rownames_to_column("question") %>%
  mutate(order = 1:nrow(.)) %>%
  gather(factor, loading, -question, -order)
```

```{r, fig.width = 3, fig.asp = 1}
d_all_all_loadings %>%
  ggplot(aes(x = factor, y = reorder(question, desc(order)), fill = loading)) +
  geom_tile(color = "black") +
  geom_text(aes(label = format(round(loading, 2), nsmall = 2)), size = 3) +
  scale_fill_distiller(palette = "RdYlBu", limits = c(-1, 1)) +
  labs(title = "All studies: All sites", x = "factor", y = "question")
```

```{r}
d_all_all_scales <- d_all_all_loadings %>%
  group_by(factor) %>% 
  top_n(3, loading) %>%
  ungroup()
```

```{r, fig.width = 3, fig.asp = 1}
d_all_all_loadings %>%
  ggplot(aes(x = factor, y = reorder(question, desc(order)), fill = loading)) +
  geom_tile(color = "black") +
  geom_tile(data = d_all_all_scales %>%
              mutate(factorlab = recode_factor(factor,
                                               "MR1" = "Synesthesia",
                                               "MR2" = "Aesthetic involvement in nature",
                                               "MR3" = "Altered states of consciousness (a)",
                                               "MR4" = "Imaginative involvement",
                                               "MR5" = "Sensed presence",
                                               "MR6" = "Altered states of consciousness (b)")),
            aes(fill = NULL, color = factorlab), alpha = 0, size = 1) +
  geom_text(aes(label = format(round(loading, 2), nsmall = 2)), size = 3) +
  scale_fill_distiller(palette = "RdYlBu", limits = c(-1, 1)) +
  scale_color_brewer(palette = "Set1") +
  labs(title = "All studies: All sites", x = "factor", y = "question",
       fill = "Factor loading", color = "MODTAS factor") +
  theme_minimal()
```

# Subscales

## Study 1

### Reliability

```{r}
# reliability
bind_rows(MR1 = summary(alpha(d_all_abs %>%
                                rownames_to_column("subject_id") %>% 
                                filter(grepl("s1_", subject_id)) %>%
                                column_to_rownames("subject_id") %>%
                                select(d_all_all_scales %>% 
                                         filter(factor == "MR1") %>% 
                                         pull(question)))),
          MR2 = summary(alpha(d_all_abs %>%
                                rownames_to_column("subject_id") %>% 
                                filter(grepl("s1_", subject_id)) %>%
                                column_to_rownames("subject_id") %>%
                                select(d_all_all_scales %>% 
                                         filter(factor == "MR2") %>% 
                                         pull(question)))),
          MR3 = summary(alpha(d_all_abs %>%
                                rownames_to_column("subject_id") %>% 
                                filter(grepl("s1_", subject_id)) %>%
                                column_to_rownames("subject_id") %>%
                                select(d_all_all_scales %>% 
                                         filter(factor == "MR3") %>% 
                                         pull(question)))),
          MR4 = summary(alpha(d_all_abs %>%
                                rownames_to_column("subject_id") %>% 
                                filter(grepl("s1_", subject_id)) %>%
                                column_to_rownames("subject_id") %>%
                                select(d_all_all_scales %>% 
                                         filter(factor == "MR4") %>% 
                                         pull(question)))),
          MR5 = summary(alpha(d_all_abs %>%
                                rownames_to_column("subject_id") %>% 
                                filter(grepl("s1_", subject_id)) %>%
                                column_to_rownames("subject_id") %>%
                                select(d_all_all_scales %>% 
                                         filter(factor == "MR5") %>% 
                                         pull(question)))),
          MR6 = summary(alpha(d_all_abs %>%
                                rownames_to_column("subject_id") %>% 
                                filter(grepl("s1_", subject_id)) %>%
                                column_to_rownames("subject_id") %>%
                                select(d_all_all_scales %>% 
                                         filter(factor == "MR6") %>% 
                                         pull(question)))))

```


### Relationships (analog to Table S20)

```{r}
d1_abs_subscales <- d1_abs %>%
  rownames_to_column("subject_id") %>%
  gather(question, response, -subject_id) %>%
  mutate(question = gsub("_abs", "", question)) %>%
  left_join(d_all_all_scales %>% distinct(question, factor)) %>% 
  filter(!is.na(factor)) %>%
  mutate(factorlab = recode_factor(factor,
                                   "MR1" = "Synesthesia",
                                   "MR2" = "Aesthetic involvement in nature",
                                   "MR3" = "Altered states of consciousness (a)",
                                   "MR4" = "Imaginative involvement",
                                   "MR5" = "Sensed presence",
                                   "MR6" = "Altered states of consciousness (b)")) %>% 
  group_by(subject_id, factor, factorlab) %>%
  summarise(score = mean(response, na.rm = T)) %>%
  ungroup() %>%
  mutate(subject_id = as.numeric(subject_id)) %>%
  left_join(d1 %>% distinct(subject_id, country)) %>%
  group_by(country, factor, factorlab) %>%
  mutate(score = scale(score)) %>%
  ungroup() %>%
  select(-country, -factor) %>%
  spread(factorlab, score) %>%
  mutate_all(as.numeric) %>%
  left_join(d1_fortab)
```

```{r}
# r104_spev <- lm(`Spiritual Events` ~ `Porosity Vignettes`,
#                data = d1_abs_subscales)

r105_spev_MR1 <- lm(`Spiritual Events` ~ Synesthesia,
                    data = d1_abs_subscales)
r105_spev_MR2 <- lm(`Spiritual Events` ~ `Aesthetic involvement in nature`,
                    data = d1_abs_subscales)
r105_spev_MR3 <- lm(`Spiritual Events` ~ `Altered states of consciousness (a)`,
                    data = d1_abs_subscales)
r105_spev_MR4 <- lm(`Spiritual Events` ~ `Imaginative involvement`,
                    data = d1_abs_subscales)
r105_spev_MR5 <- lm(`Spiritual Events` ~ `Sensed presence`,
                    data = d1_abs_subscales)
r105_spev_MR6 <- lm(`Spiritual Events` ~ `Altered states of consciousness (b)`,
                    data = d1_abs_subscales)

r106_spev_MR1 <- lm(`Spiritual Events` ~ `Porosity Vignettes` * Synesthesia,
                    data = d1_abs_subscales)
r106_spev_MR2 <- lm(`Spiritual Events` ~ `Porosity Vignettes` * `Aesthetic involvement in nature`,
                    data = d1_abs_subscales)
r106_spev_MR3 <- lm(`Spiritual Events` ~ `Porosity Vignettes` * `Altered states of consciousness (a)`,
                    data = d1_abs_subscales)
r106_spev_MR4 <- lm(`Spiritual Events` ~ `Porosity Vignettes` * `Imaginative involvement`,
                    data = d1_abs_subscales)
r106_spev_MR5 <- lm(`Spiritual Events` ~ `Porosity Vignettes` * `Sensed presence`,
                    data = d1_abs_subscales)
r106_spev_MR6 <- lm(`Spiritual Events` ~ `Porosity Vignettes` * `Altered states of consciousness (b)`,
                    data = d1_abs_subscales)

# r107_spev <- lm(`Spiritual Events` ~ Country * Site * Religion 
#                + `Porosity Vignettes` + Country:`Porosity Vignettes`,
#                data = d1_abs_subscales)

r108_spev_MR1 <- lm(`Spiritual Events` ~ Country * Site * Religion 
                    + Synesthesia + Country:Synesthesia,
                    data = d1_abs_subscales)
r108_spev_MR2 <- lm(`Spiritual Events` ~ Country * Site * Religion 
                    + `Aesthetic involvement in nature` + Country:`Aesthetic involvement in nature`,
                    data = d1_abs_subscales)
r108_spev_MR3 <- lm(`Spiritual Events` ~ Country * Site * Religion 
                    + `Altered states of consciousness (a)` + Country:`Altered states of consciousness (a)`,
                    data = d1_abs_subscales)
r108_spev_MR4 <- lm(`Spiritual Events` ~ Country * Site * Religion 
                    + `Imaginative involvement` + Country:`Imaginative involvement`,
                    data = d1_abs_subscales)
r108_spev_MR5 <- lm(`Spiritual Events` ~ Country * Site * Religion 
                    + `Sensed presence` + Country:`Sensed presence`,
                    data = d1_abs_subscales)
r108_spev_MR6 <- lm(`Spiritual Events` ~ Country * Site * Religion 
                    + `Altered states of consciousness (b)` + Country:`Altered states of consciousness (b)`,
                    data = d1_abs_subscales)

r109_spev_MR1 <- lm(`Spiritual Events` ~ Country * Site * Religion 
                    + `Porosity Vignettes` + Country:`Porosity Vignettes`
                    + Synesthesia + Country:Synesthesia
                    + `Porosity Vignettes`:Synesthesia 
                    + Country:`Porosity Vignettes`:Synesthesia,
                    data = d1_abs_subscales)
r109_spev_MR2 <- lm(`Spiritual Events` ~ Country * Site * Religion 
                    + `Porosity Vignettes` + Country:`Porosity Vignettes`
                    + `Aesthetic involvement in nature` + Country:`Aesthetic involvement in nature`
                    + `Porosity Vignettes`:`Aesthetic involvement in nature` 
                    + Country:`Porosity Vignettes`:`Aesthetic involvement in nature`,
                    data = d1_abs_subscales)
r109_spev_MR3 <- lm(`Spiritual Events` ~ Country * Site * Religion 
                    + `Porosity Vignettes` + Country:`Porosity Vignettes`
                    + `Altered states of consciousness (a)` + Country:`Altered states of consciousness (a)`
                    + `Porosity Vignettes`:`Altered states of consciousness (a)` 
                    + Country:`Porosity Vignettes`:`Altered states of consciousness (a)`,
                    data = d1_abs_subscales)
r109_spev_MR4 <- lm(`Spiritual Events` ~ Country * Site * Religion 
                    + `Porosity Vignettes` + Country:`Porosity Vignettes`
                    + `Imaginative involvement` + Country:`Imaginative involvement`
                    + `Porosity Vignettes`:`Imaginative involvement` 
                    + Country:`Porosity Vignettes`:`Imaginative involvement`,
                    data = d1_abs_subscales)
r109_spev_MR5 <- lm(`Spiritual Events` ~ Country * Site * Religion 
                    + `Porosity Vignettes` + Country:`Porosity Vignettes`
                    + `Sensed presence` + Country:`Sensed presence`
                    + `Porosity Vignettes`:`Sensed presence` 
                    + Country:`Porosity Vignettes`:`Sensed presence`,
                    data = d1_abs_subscales)
r109_spev_MR6 <- lm(`Spiritual Events` ~ Country * Site * Religion 
                    + `Porosity Vignettes` + Country:`Porosity Vignettes`
                    + `Altered states of consciousness (b)` + Country:`Altered states of consciousness (b)`
                    + `Porosity Vignettes`:`Altered states of consciousness (b)` 
                    + Country:`Porosity Vignettes`:`Altered states of consciousness (b)`,
                    data = d1_abs_subscales)

r110_spev_MR1 <- lm(`Spiritual Events` ~ Country * Site * Religion 
                   + `Porosity Vignettes` + Country:`Porosity Vignettes`
                   + Synesthesia + Country:Synesthesia
                   + `Porosity Vignettes`:Synesthesia
                   + Country:`Porosity Vignettes`:Synesthesia
                   + Gender + Age + Education,
                   data = d1_abs_subscales)
r110_spev_MR2 <- lm(`Spiritual Events` ~ Country * Site * Religion 
                   + `Porosity Vignettes` + Country:`Porosity Vignettes`
                   + `Aesthetic involvement in nature` + Country:`Aesthetic involvement in nature`
                   + `Porosity Vignettes`:`Aesthetic involvement in nature`
                   + Country:`Porosity Vignettes`:`Aesthetic involvement in nature`
                   + Gender + Age + Education,
                   data = d1_abs_subscales)
r110_spev_MR3 <- lm(`Spiritual Events` ~ Country * Site * Religion 
                   + `Porosity Vignettes` + Country:`Porosity Vignettes`
                   + `Altered states of consciousness (a)` + Country:`Altered states of consciousness (a)`
                   + `Porosity Vignettes`:`Altered states of consciousness (a)`
                   + Country:`Porosity Vignettes`:`Altered states of consciousness (a)`
                   + Gender + Age + Education,
                   data = d1_abs_subscales)
r110_spev_MR4 <- lm(`Spiritual Events` ~ Country * Site * Religion 
                   + `Porosity Vignettes` + Country:`Porosity Vignettes`
                   + `Imaginative involvement` + Country:`Imaginative involvement`
                   + `Porosity Vignettes`:`Imaginative involvement`
                   + Country:`Porosity Vignettes`:`Imaginative involvement`
                   + Gender + Age + Education,
                   data = d1_abs_subscales)
r110_spev_MR5 <- lm(`Spiritual Events` ~ Country * Site * Religion 
                   + `Porosity Vignettes` + Country:`Porosity Vignettes`
                   + `Sensed presence` + Country:`Sensed presence`
                   + `Porosity Vignettes`:`Sensed presence`
                   + Country:`Porosity Vignettes`:`Sensed presence`
                   + Gender + Age + Education,
                   data = d1_abs_subscales)
r110_spev_MR6 <- lm(`Spiritual Events` ~ Country * Site * Religion 
                   + `Porosity Vignettes` + Country:`Porosity Vignettes`
                   + `Altered states of consciousness (b)` + Country:`Altered states of consciousness (b)`
                   + `Porosity Vignettes`:`Altered states of consciousness (b)`
                   + Country:`Porosity Vignettes`:`Altered states of consciousness (b)`
                   + Gender + Age + Education,
                   data = d1_abs_subscales)
```

```{r, include = F}
# # run manually to check convergence
# # summary(r104_spev)
# summary(r105_spev)
# summary(r106_spev)
# # summary(r107_spev)
# summary(r108_spev)
# summary(r109_spev)
# summary(r110_spev)
```

```{r, results = "hide"}
table_s20_subscales <- stargazer(
  #r104_spev, 
  r105_spev_MR1, r105_spev_MR2, r105_spev_MR3, r105_spev_MR4, r105_spev_MR5, r105_spev_MR6,
  r106_spev_MR1, r106_spev_MR2, r106_spev_MR3, r106_spev_MR4, r106_spev_MR5, r106_spev_MR6,
  #r107_spev, 
  r108_spev_MR1, r108_spev_MR2, r108_spev_MR3, r108_spev_MR4, r108_spev_MR5, r108_spev_MR6,
  r109_spev_MR1, r109_spev_MR2, r109_spev_MR3, r109_spev_MR4, r109_spev_MR5, r109_spev_MR6,
  r110_spev_MR1, r110_spev_MR2, r110_spev_MR3, r110_spev_MR4, r110_spev_MR5, r110_spev_MR6,
  type = "html",
  ci = T,
  report = c("vcsp"),
  digits = 2,
  digits.extra = 1000,
  omit.table.layout = "n",
  # order = c("^`Porosity Vignettes`$",
  #           "^Absorption$",
  #           "^`Porosity Vignettes`:Absorption$"),
  # dep.var.labels.include = F,
  dep.var.caption = "",
  single.row = T)

cat(sub_interact_fun(table_s20_subscales), 
    file = "./stargazer_tables/table_s20_subscales.html")
```

```{r}
# htmltools::includeHTML("./stargazer_tables/table_s20_subscales.html")
```

```{r, results = "hide"}
table_s20_subscales_MR1 <- stargazer(#r104_spev,
                                     r105_spev_MR1, r106_spev_MR1, 
                                     #r107_spev, 
                                     r108_spev_MR1, r109_spev_MR1, r110_spev_MR1, 
                                     type = "html",
                                     ci = T,
                                     report = c("vcsp"),
                                     digits = 2,
                                     digits.extra = 1000,
                                     omit.table.layout = "n",
                                     order = c("^`Porosity Vignettes`$",
                                               "^Synesthesia$",
                                               "^`Porosity Vignettes`:Synesthesia$"),
                                     dep.var.labels.include = F,
                                     dep.var.caption = "",
                                     single.row = T)

cat(sub_interact_fun(table_s20_subscales_MR1), 
    file = "./stargazer_tables/table_s20_subscales_MR1.html")
```

```{r}
htmltools::includeHTML("./stargazer_tables/table_s20_subscales_MR1.html")
```

```{r, results = "hide"}
table_s20_subscales_MR2 <- stargazer(#r104_spev,
                                     r105_spev_MR2, r106_spev_MR2, 
                                     #r107_spev, 
                                     r108_spev_MR2, r109_spev_MR2, r110_spev_MR2, 
                                     type = "html",
                                     ci = T,
                                     report = c("vcsp"),
                                     digits = 2,
                                     digits.extra = 1000,
                                     omit.table.layout = "n",
                                     order = c("^`Porosity Vignettes`$",
                                               "^`Aesthetic involvement in nature`$",
                                               "^`Porosity Vignettes`:`Aesthetic involvement in nature`$"),
                                     dep.var.labels.include = F,
                                     dep.var.caption = "",
                                     single.row = T)

cat(sub_interact_fun(table_s20_subscales_MR2), 
    file = "./stargazer_tables/table_s20_subscales_MR2.html")
```

```{r}
htmltools::includeHTML("./stargazer_tables/table_s20_subscales_MR2.html")
```

```{r, results = "hide"}
table_s20_subscales_MR3 <- stargazer(#r104_spev,
                                     r105_spev_MR3, r106_spev_MR3, 
                                     #r107_spev, 
                                     r108_spev_MR3, r109_spev_MR3, r110_spev_MR3, 
                                     type = "html",
                                     ci = T,
                                     report = c("vcsp"),
                                     digits = 2,
                                     digits.extra = 1000,
                                     omit.table.layout = "n",
                                     order = c("^`Porosity Vignettes`$",
                                               "^`Altered states of consciousness \\(a\\)`$",
                                               "^`Porosity Vignettes`:`Altered states of consciousness \\(a\\)`$"),
                                     dep.var.labels.include = F,
                                     dep.var.caption = "",
                                     single.row = T)

cat(sub_interact_fun(table_s20_subscales_MR3), 
    file = "./stargazer_tables/table_s20_subscales_MR3.html")
```

```{r}
htmltools::includeHTML("./stargazer_tables/table_s20_subscales_MR3.html")
```

```{r, results = "hide"}
table_s20_subscales_MR4 <- stargazer(#r104_spev,
                                     r105_spev_MR4, r106_spev_MR4, 
                                     #r107_spev, 
                                     r108_spev_MR4, r109_spev_MR4, r110_spev_MR4, 
                                     type = "html",
                                     ci = T,
                                     report = c("vcsp"),
                                     digits = 2,
                                     digits.extra = 1000,
                                     omit.table.layout = "n",
                                     order = c("^`Porosity Vignettes`$",
                                               "^`Imaginative involvement`$",
                                               "^`Porosity Vignettes`:`Imaginative involvement`$"),
                                     dep.var.labels.include = F,
                                     dep.var.caption = "",
                                     single.row = T)

cat(sub_interact_fun(table_s20_subscales_MR4), 
    file = "./stargazer_tables/table_s20_subscales_MR4.html")
```

```{r}
htmltools::includeHTML("./stargazer_tables/table_s20_subscales_MR4.html")
```

```{r, results = "hide"}
table_s20_subscales_MR5 <- stargazer(#r104_spev,
                                     r105_spev_MR5, r106_spev_MR5, 
                                     #r107_spev, 
                                     r108_spev_MR5, r109_spev_MR5, r110_spev_MR5, 
                                     type = "html",
                                     ci = T,
                                     report = c("vcsp"),
                                     digits = 2,
                                     digits.extra = 1000,
                                     omit.table.layout = "n",
                                     order = c("^`Porosity Vignettes`$",
                                               "^`Sensed presence`$",
                                               "^`Porosity Vignettes`:`Sensed presence`$"),
                                     dep.var.labels.include = F,
                                     dep.var.caption = "",
                                     single.row = T)

cat(sub_interact_fun(table_s20_subscales_MR5), 
    file = "./stargazer_tables/table_s20_subscales_MR5.html")
```

```{r}
htmltools::includeHTML("./stargazer_tables/table_s20_subscales_MR5.html")
```

```{r, results = "hide"}
table_s20_subscales_MR6 <- stargazer(#r104_spev,
                                     r105_spev_MR6, r106_spev_MR6, 
                                     #r107_spev, 
                                     r108_spev_MR6, r109_spev_MR6, r110_spev_MR6, 
                                     type = "html",
                                     ci = T,
                                     report = c("vcsp"),
                                     digits = 2,
                                     digits.extra = 1000,
                                     omit.table.layout = "n",
                                     order = c("^`Porosity Vignettes`$",
                                               "^`Altered states of consciousness \\(b\\)`$",
                                               "^`Porosity Vignettes`:`Altered states of consciousness \\(b\\)`$"),
                                     dep.var.labels.include = F,
                                     dep.var.caption = "",
                                     single.row = T)

cat(sub_interact_fun(table_s20_subscales_MR6), 
    file = "./stargazer_tables/table_s20_subscales_MR6.html")
```

```{r}
htmltools::includeHTML("./stargazer_tables/table_s20_subscales_MR6.html")
```

## Study 3

### Reliability

```{r}
# reliability
bind_rows(MR1 = summary(alpha(d_all_abs %>%
                                rownames_to_column("subject_id") %>% 
                                filter(grepl("s3_", subject_id)) %>%
                                column_to_rownames("subject_id") %>%
                                select(d_all_all_scales %>% 
                                         filter(factor == "MR1") %>% 
                                         pull(question)))),
          MR2 = summary(alpha(d_all_abs %>%
                                rownames_to_column("subject_id") %>% 
                                filter(grepl("s3_", subject_id)) %>%
                                column_to_rownames("subject_id") %>%
                                select(d_all_all_scales %>% 
                                         filter(factor == "MR2") %>% 
                                         pull(question)))),
          MR3 = summary(alpha(d_all_abs %>%
                                rownames_to_column("subject_id") %>% 
                                filter(grepl("s3_", subject_id)) %>%
                                column_to_rownames("subject_id") %>%
                                select(d_all_all_scales %>% 
                                         filter(factor == "MR3") %>% 
                                         pull(question)))),
          MR4 = summary(alpha(d_all_abs %>%
                                rownames_to_column("subject_id") %>% 
                                filter(grepl("s3_", subject_id)) %>%
                                column_to_rownames("subject_id") %>%
                                select(d_all_all_scales %>% 
                                         filter(factor == "MR4") %>% 
                                         pull(question)))),
          MR5 = summary(alpha(d_all_abs %>%
                                rownames_to_column("subject_id") %>% 
                                filter(grepl("s3_", subject_id)) %>%
                                column_to_rownames("subject_id") %>%
                                select(d_all_all_scales %>% 
                                         filter(factor == "MR5") %>% 
                                         pull(question)))),
          MR6 = summary(alpha(d_all_abs %>%
                                rownames_to_column("subject_id") %>% 
                                filter(grepl("s3_", subject_id)) %>%
                                column_to_rownames("subject_id") %>%
                                select(d_all_all_scales %>% 
                                         filter(factor == "MR6") %>% 
                                         pull(question)))))

```


### Relationships (analog to Table S32)

```{r}
d3_abs_subscales <- d3_abs %>%
  rownames_to_column("subject_id") %>%
  gather(question, response, -subject_id) %>%
  mutate(question = gsub("_abs", "", question)) %>%
  left_join(d_all_all_scales %>% distinct(question, factor)) %>% 
  filter(!is.na(factor)) %>%
  mutate(factorlab = recode_factor(factor,
                                   "MR1" = "Synesthesia",
                                   "MR2" = "Aesthetic involvement in nature",
                                   "MR3" = "Altered states of consciousness (a)",
                                   "MR4" = "Imaginative involvement",
                                   "MR5" = "Sensed presence",
                                   "MR6" = "Altered states of consciousness (b)")) %>% 
  group_by(subject_id, factor, factorlab) %>%
  summarise(score = mean(response, na.rm = T)) %>%
  ungroup() %>%
  mutate(subject_id = as.numeric(subject_id)) %>%
  left_join(d3 %>% distinct(subject_id, country)) %>%
  group_by(country, factor, factorlab) %>%
  mutate(score = scale(score)) %>%
  ungroup() %>%
  select(-country, -factor) %>%
  spread(factorlab, score) %>%
  mutate_all(as.numeric) %>%
  left_join(d3_fortab)
```

```{r}
r103_spev_MR1 <- lm(`Spiritual Events` ~ `Synesthesia`,
                    data = d3_abs_subscales)
r103_spev_MR2 <- lm(`Spiritual Events` ~ `Aesthetic involvement in nature`,
                    data = d3_abs_subscales)
r103_spev_MR3 <- lm(`Spiritual Events` ~ `Altered states of consciousness (a)`,
                    data = d3_abs_subscales)
r103_spev_MR4 <- lm(`Spiritual Events` ~ `Imaginative involvement`,
                    data = d3_abs_subscales)
r103_spev_MR5 <- lm(`Spiritual Events` ~ `Sensed presence`,
                    data = d3_abs_subscales)
r103_spev_MR6 <- lm(`Spiritual Events` ~ `Altered states of consciousness (b)`,
                    data = d3_abs_subscales)

r104_spev_MR1 <- lm(`Spiritual Events` ~ Country * `Synesthesia`,
                    data = d3_abs_subscales)
r104_spev_MR2 <- lm(`Spiritual Events` ~ Country * `Aesthetic involvement in nature`,
                    data = d3_abs_subscales)
r104_spev_MR3 <- lm(`Spiritual Events` ~ Country * `Altered states of consciousness (a)`,
                    data = d3_abs_subscales)
r104_spev_MR4 <- lm(`Spiritual Events` ~ Country * `Imaginative involvement`,
                    data = d3_abs_subscales)
r104_spev_MR5 <- lm(`Spiritual Events` ~ Country * `Sensed presence`,
                    data = d3_abs_subscales)
r104_spev_MR6 <- lm(`Spiritual Events` ~ Country * `Altered states of consciousness (b)`,
                    data = d3_abs_subscales)

r105_spev_MR1 <- lm(`Spiritual Events` ~ Country * `Synesthesia`
                    + Gender + Age,  
                    data = d3_abs_subscales)
r105_spev_MR2 <- lm(`Spiritual Events` ~ Country * `Aesthetic involvement in nature`
                    + Gender + Age,  
                    data = d3_abs_subscales)
r105_spev_MR3 <- lm(`Spiritual Events` ~ Country * `Altered states of consciousness (a)`
                    + Gender + Age,  
                    data = d3_abs_subscales)
r105_spev_MR4 <- lm(`Spiritual Events` ~ Country * `Imaginative involvement`
                    + Gender + Age,  
                    data = d3_abs_subscales)
r105_spev_MR5 <- lm(`Spiritual Events` ~ Country * `Sensed presence`
                    + Gender + Age,  
                    data = d3_abs_subscales)
r105_spev_MR6 <- lm(`Spiritual Events` ~ Country * `Altered states of consciousness (b)`
                    + Gender + Age,  
                    data = d3_abs_subscales)
```

```{r}
r103_dse_MR1 <- lm(`Daily Spiritual Experiences` ~ `Synesthesia`,
                    data = d3_abs_subscales)
r103_dse_MR2 <- lm(`Daily Spiritual Experiences` ~ `Aesthetic involvement in nature`,
                    data = d3_abs_subscales)
r103_dse_MR3 <- lm(`Daily Spiritual Experiences` ~ `Altered states of consciousness (a)`,
                    data = d3_abs_subscales)
r103_dse_MR4 <- lm(`Daily Spiritual Experiences` ~ `Imaginative involvement`,
                    data = d3_abs_subscales)
r103_dse_MR5 <- lm(`Daily Spiritual Experiences` ~ `Sensed presence`,
                    data = d3_abs_subscales)
r103_dse_MR6 <- lm(`Daily Spiritual Experiences` ~ `Altered states of consciousness (b)`,
                    data = d3_abs_subscales)

r104_dse_MR1 <- lm(`Daily Spiritual Experiences` ~ Country * `Synesthesia`,
                    data = d3_abs_subscales)
r104_dse_MR2 <- lm(`Daily Spiritual Experiences` ~ Country * `Aesthetic involvement in nature`,
                    data = d3_abs_subscales)
r104_dse_MR3 <- lm(`Daily Spiritual Experiences` ~ Country * `Altered states of consciousness (a)`,
                    data = d3_abs_subscales)
r104_dse_MR4 <- lm(`Daily Spiritual Experiences` ~ Country * `Imaginative involvement`,
                    data = d3_abs_subscales)
r104_dse_MR5 <- lm(`Daily Spiritual Experiences` ~ Country * `Sensed presence`,
                    data = d3_abs_subscales)
r104_dse_MR6 <- lm(`Daily Spiritual Experiences` ~ Country * `Altered states of consciousness (b)`,
                    data = d3_abs_subscales)

r105_dse_MR1 <- lm(`Daily Spiritual Experiences` ~ Country * `Synesthesia`
                    + Gender + Age,  
                    data = d3_abs_subscales)
r105_dse_MR2 <- lm(`Daily Spiritual Experiences` ~ Country * `Aesthetic involvement in nature`
                    + Gender + Age,  
                    data = d3_abs_subscales)
r105_dse_MR3 <- lm(`Daily Spiritual Experiences` ~ Country * `Altered states of consciousness (a)`
                    + Gender + Age,  
                    data = d3_abs_subscales)
r105_dse_MR4 <- lm(`Daily Spiritual Experiences` ~ Country * `Imaginative involvement`
                    + Gender + Age,  
                    data = d3_abs_subscales)
r105_dse_MR5 <- lm(`Daily Spiritual Experiences` ~ Country * `Sensed presence`
                    + Gender + Age,  
                    data = d3_abs_subscales)
r105_dse_MR6 <- lm(`Daily Spiritual Experiences` ~ Country * `Altered states of consciousness (b)`
                    + Gender + Age,  
                    data = d3_abs_subscales)
```


```{r, results = "hide"}
table_s32_subscales <- stargazer(
  r103_spev_MR1, r103_spev_MR2, r103_spev_MR3, r103_spev_MR4, r103_spev_MR5, r103_spev_MR6,  
  r104_spev_MR1, r104_spev_MR2, r104_spev_MR3, r104_spev_MR4, r104_spev_MR5, r104_spev_MR6,  
  r105_spev_MR1, r105_spev_MR2, r105_spev_MR3, r105_spev_MR4, r105_spev_MR5, r105_spev_MR6,  
  r103_dse_MR1, r103_dse_MR2, r103_dse_MR3, r103_dse_MR4, r103_dse_MR5, r103_dse_MR6,  
  r104_dse_MR1, r104_dse_MR2, r104_dse_MR3, r104_dse_MR4, r104_dse_MR5, r104_dse_MR6,  
  r105_dse_MR1, r105_dse_MR2, r105_dse_MR3, r105_dse_MR4, r105_dse_MR5, r105_dse_MR6,  
                       type = "html",
                       ci = T,
                       report = c("vcsp"),
                       digits = 2,
                       digits.extra = 1000,
                       omit.table.layout = "n",
                       order = c("^`Synesthesia`$",
                                 "^`Aesthetic involvement in nature`$",
                                 "^`Altered states of consciousness \\(a\\)`$",
                                 "^`Imaginative involvement`$",
                                 "^`Sensed presence`$",
                                 "^`Altered states of consciousness \\(b\\)`$"),
                       # dep.var.labels.include = F,
                       dep.var.caption = "",
                       single.row = T)

cat(sub_interact_fun(table_s32_subscales), 
    file = "./stargazer_tables/table_s32_subscales.html")
```

```{r}
# htmltools::includeHTML("./stargazer_tables/table_s32_subscales.html")
```

```{r, results = "hide"}
table_s32_subscales_MR1 <- stargazer(r103_spev_MR1, r104_spev_MR1, r105_spev_MR1, 
                                     r103_dse_MR1, r104_dse_MR1, r105_dse_MR1, 
                                     type = "html",
                                     ci = T,
                                     report = c("vcsp"),
                                     digits = 2,
                                     digits.extra = 1000,
                                     omit.table.layout = "n",
                                     order = c("^Synesthesia$",
                                               "^`Aesthetic involvement in nature`$",
                                               "^`Altered states of consciousness \\(a\\)`$",
                                               "^`Imaginative involvement`$",
                                               "^`Sensed presence`$",
                                               "^`Altered states of consciousness \\(b\\)`$"),
                                     # dep.var.labels.include = F,
                                     dep.var.caption = "",
                                     single.row = T)

cat(sub_interact_fun(table_s32_subscales_MR1), 
    file = "./stargazer_tables/table_s32_subscales_MR1.html")
```

```{r}
htmltools::includeHTML("./stargazer_tables/table_s32_subscales_MR1.html")
```

```{r, results = "hide"}
table_s32_subscales_MR2 <- stargazer(r103_spev_MR2, r104_spev_MR2, r105_spev_MR2, 
                                     r103_dse_MR2, r104_dse_MR2, r105_dse_MR2, 
                                     type = "html",
                                     ci = T,
                                     report = c("vcsp"),
                                     digits = 2,
                                     digits.extra = 1000,
                                     omit.table.layout = "n",
                                     order = c("^Synesthesia$",
                                               "^`Aesthetic involvement in nature`$",
                                               "^`Altered states of consciousness \\(a\\)`$",
                                               "^`Imaginative involvement`$",
                                               "^`Sensed presence`$",
                                               "^`Altered states of consciousness \\(b\\)`$"),
                                     # dep.var.labels.include = F,
                                     dep.var.caption = "",
                                     single.row = T)

cat(sub_interact_fun(table_s32_subscales_MR2), 
    file = "./stargazer_tables/table_s32_subscales_MR2.html")
```

```{r}
htmltools::includeHTML("./stargazer_tables/table_s32_subscales_MR2.html")
```

```{r, results = "hide"}
table_s32_subscales_MR3 <- stargazer(r103_spev_MR3, r104_spev_MR3, r105_spev_MR3, 
                                     r103_dse_MR3, r104_dse_MR3, r105_dse_MR3, 
                                     type = "html",
                                     ci = T,
                                     report = c("vcsp"),
                                     digits = 2,
                                     digits.extra = 1000,
                                     omit.table.layout = "n",
                                     order = c("^Synesthesia$",
                                               "^`Aesthetic involvement in nature`$",
                                               "^`Altered states of consciousness \\(a\\)`$",
                                               "^`Imaginative involvement`$",
                                               "^`Sensed presence`$",
                                               "^`Altered states of consciousness \\(b\\)`$"),
                                     # dep.var.labels.include = F,
                                     dep.var.caption = "",
                                     single.row = T)

cat(sub_interact_fun(table_s32_subscales_MR3), 
    file = "./stargazer_tables/table_s32_subscales_MR3.html")
```

```{r}
htmltools::includeHTML("./stargazer_tables/table_s32_subscales_MR3.html")
```

```{r, results = "hide"}
table_s32_subscales_MR4 <- stargazer(r103_spev_MR4, r104_spev_MR4, r105_spev_MR4, 
                                     r103_dse_MR4, r104_dse_MR4, r105_dse_MR4, 
                                     type = "html",
                                     ci = T,
                                     report = c("vcsp"),
                                     digits = 2,
                                     digits.extra = 1000,
                                     omit.table.layout = "n",
                                     order = c("^Synesthesia$",
                                               "^`Aesthetic involvement in nature`$",
                                               "^`Altered states of consciousness \\(a\\)`$",
                                               "^`Imaginative involvement`$",
                                               "^`Sensed presence`$",
                                               "^`Altered states of consciousness \\(b\\)`$"),
                                     # dep.var.labels.include = F,
                                     dep.var.caption = "",
                                     single.row = T)

cat(sub_interact_fun(table_s32_subscales_MR4), 
    file = "./stargazer_tables/table_s32_subscales_MR4.html")
```

```{r}
htmltools::includeHTML("./stargazer_tables/table_s32_subscales_MR4.html")
```

```{r, results = "hide"}
table_s32_subscales_MR5 <- stargazer(r103_spev_MR5, r104_spev_MR5, r105_spev_MR5, 
                                     r103_dse_MR5, r104_dse_MR5, r105_dse_MR5, 
                                     type = "html",
                                     ci = T,
                                     report = c("vcsp"),
                                     digits = 2,
                                     digits.extra = 1000,
                                     omit.table.layout = "n",
                                     order = c("^Synesthesia$",
                                               "^`Aesthetic involvement in nature`$",
                                               "^`Altered states of consciousness \\(a\\)`$",
                                               "^`Imaginative involvement`$",
                                               "^`Sensed presence`$",
                                               "^`Altered states of consciousness \\(b\\)`$"),
                                     # dep.var.labels.include = F,
                                     dep.var.caption = "",
                                     single.row = T)

cat(sub_interact_fun(table_s32_subscales_MR5), 
    file = "./stargazer_tables/table_s32_subscales_MR5.html")
```

```{r}
htmltools::includeHTML("./stargazer_tables/table_s32_subscales_MR5.html")
```

```{r, results = "hide"}
table_s32_subscales_MR6 <- stargazer(r103_spev_MR6, r104_spev_MR6, r105_spev_MR6, 
                                     r103_dse_MR6, r104_dse_MR6, r105_dse_MR6, 
                                     type = "html",
                                     ci = T,
                                     report = c("vcsp"),
                                     digits = 2,
                                     digits.extra = 1000,
                                     omit.table.layout = "n",
                                     order = c("^Synesthesia$",
                                               "^`Aesthetic involvement in nature`$",
                                               "^`Altered states of consciousness \\(a\\)`$",
                                               "^`Imaginative involvement`$",
                                               "^`Sensed presence`$",
                                               "^`Altered states of consciousness \\(b\\)`$"),
                                     # dep.var.labels.include = F,
                                     dep.var.caption = "",
                                     single.row = T)

cat(sub_interact_fun(table_s32_subscales_MR6), 
    file = "./stargazer_tables/table_s32_subscales_MR6.html")
```

```{r}
htmltools::includeHTML("./stargazer_tables/table_s32_subscales_MR6.html")
```

## Study 4

### Reliability

```{r}
# reliability
bind_rows(MR1 = summary(alpha(d_all_abs %>%
                                rownames_to_column("subject_id") %>% 
                                filter(grepl("s4_", subject_id)) %>%
                                column_to_rownames("subject_id") %>%
                                select(d_all_all_scales %>% 
                                         filter(factor == "MR1") %>% 
                                         pull(question)))),
          MR2 = summary(alpha(d_all_abs %>%
                                rownames_to_column("subject_id") %>% 
                                filter(grepl("s4_", subject_id)) %>%
                                column_to_rownames("subject_id") %>%
                                select(d_all_all_scales %>% 
                                         filter(factor == "MR2") %>% 
                                         pull(question)))),
          MR3 = summary(alpha(d_all_abs %>%
                                rownames_to_column("subject_id") %>% 
                                filter(grepl("s4_", subject_id)) %>%
                                column_to_rownames("subject_id") %>%
                                select(d_all_all_scales %>% 
                                         filter(factor == "MR3") %>% 
                                         pull(question)))),
          MR4 = summary(alpha(d_all_abs %>%
                                rownames_to_column("subject_id") %>% 
                                filter(grepl("s4_", subject_id)) %>%
                                column_to_rownames("subject_id") %>%
                                select(d_all_all_scales %>% 
                                         filter(factor == "MR4") %>% 
                                         pull(question)))),
          MR5 = summary(alpha(d_all_abs %>%
                                rownames_to_column("subject_id") %>% 
                                filter(grepl("s4_", subject_id)) %>%
                                column_to_rownames("subject_id") %>%
                                select(d_all_all_scales %>% 
                                         filter(factor == "MR5") %>% 
                                         pull(question)))),
          MR6 = summary(alpha(d_all_abs %>%
                                rownames_to_column("subject_id") %>% 
                                filter(grepl("s4_", subject_id)) %>%
                                column_to_rownames("subject_id") %>%
                                select(d_all_all_scales %>% 
                                         filter(factor == "MR6") %>% 
                                         pull(question)))))

```


### Relationships (analog to Tables S40-S41)

```{r}
d4_abs_subscales <- d_all_abs %>%
  rownames_to_column("subject_id") %>% 
  filter(grepl("s4_", subject_id)) %>%
  gather(question, response, -subject_id) %>%
  left_join(d_all_all_scales %>% distinct(question, factor)) %>% 
  filter(!is.na(factor)) %>%
  mutate(factorlab = recode_factor(factor,
                                   "MR1" = "Synesthesia",
                                   "MR2" = "Aesthetic involvement in nature",
                                   "MR3" = "Altered states of consciousness (a)",
                                   "MR4" = "Imaginative involvement",
                                   "MR5" = "Sensed presence",
                                   "MR6" = "Altered states of consciousness (b)")) %>% 
  group_by(subject_id, factor, factorlab) %>%
  summarise(score = mean(response, na.rm = T)) %>%
  ungroup() %>%
  mutate(subject_id = as.numeric(gsub("s4_", "", subject_id))) %>%
  left_join(d4 %>% distinct(subject_id, country)) %>%
  group_by(country, factor, factorlab) %>%
  mutate(score = scale(score)) %>%
  ungroup() %>%
  select(-country, -factor) %>%
  spread(factorlab, score) %>%
  mutate_all(as.numeric) %>%
  left_join(d4_fortab_long_spirit)
```

### Analog to Table S40

```{r}
r105_spirit_MR1 <- lmer(Score ~ Scale * Synesthesia
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)

r108_spirit_MR1 <- lmer(Score ~ Scale * Country * Synesthesia
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)

r109_spirit_MR1 <- lmer(Score ~ Scale * Country * `Porosity Vignettes` * Synesthesia
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)

r110_spirit_MR1 <- lmer(Score ~ Scale * Country * `Porosity Scale` * Synesthesia
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)

r111_spirit_MR1 <- lmer(Score ~ Scale * Country * `Porosity Vignettes` * Synesthesia
                        + Gender + Age
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)

r112_spirit_MR1 <- lmer(Score ~ Scale * Country * `Porosity Scale` * Synesthesia
                        + Gender + Age
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)
```

```{r}
r105_spirit_MR2 <- lmer(Score ~ Scale * `Aesthetic involvement in nature`
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)

r108_spirit_MR2 <- lmer(Score ~ Scale * Country * `Aesthetic involvement in nature`
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)

r109_spirit_MR2 <- lmer(Score ~ Scale * Country * `Porosity Vignettes` * `Aesthetic involvement in nature`
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)

r110_spirit_MR2 <- lmer(Score ~ Scale * Country * `Porosity Scale` * `Aesthetic involvement in nature`
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)

r111_spirit_MR2 <- lmer(Score ~ Scale * Country * `Porosity Vignettes` * `Aesthetic involvement in nature`
                        + Gender + Age
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)

r112_spirit_MR2 <- lmer(Score ~ Scale * Country * `Porosity Scale` * `Aesthetic involvement in nature`
                        + Gender + Age
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)
```

```{r}
r105_spirit_MR3 <- lmer(Score ~ Scale * `Altered states of consciousness (a)`
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)

r108_spirit_MR3 <- lmer(Score ~ Scale * Country * `Altered states of consciousness (a)`
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)

r109_spirit_MR3 <- lmer(Score ~ Scale * Country * `Porosity Vignettes` * `Altered states of consciousness (a)`
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)

r110_spirit_MR3 <- lmer(Score ~ Scale * Country * `Porosity Scale` * `Altered states of consciousness (a)`
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)

r111_spirit_MR3 <- lmer(Score ~ Scale * Country * `Porosity Vignettes` * `Altered states of consciousness (a)`
                        + Gender + Age
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)

r112_spirit_MR3 <- lmer(Score ~ Scale * Country * `Porosity Scale` * `Altered states of consciousness (a)`
                        + Gender + Age
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)
```

```{r}
r105_spirit_MR4 <- lmer(Score ~ Scale * `Imaginative involvement`
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)

r108_spirit_MR4 <- lmer(Score ~ Scale * Country * `Imaginative involvement`
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)

r109_spirit_MR4 <- lmer(Score ~ Scale * Country * `Porosity Vignettes` * `Imaginative involvement`
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)

r110_spirit_MR4 <- lmer(Score ~ Scale * Country * `Porosity Scale` * `Imaginative involvement`
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)

r111_spirit_MR4 <- lmer(Score ~ Scale * Country * `Porosity Vignettes` * `Imaginative involvement`
                        + Gender + Age
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)

r112_spirit_MR4 <- lmer(Score ~ Scale * Country * `Porosity Scale` * `Imaginative involvement`
                        + Gender + Age
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)
```

```{r}
r105_spirit_MR5 <- lmer(Score ~ Scale * `Sensed presence`
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)

r108_spirit_MR5 <- lmer(Score ~ Scale * Country * `Sensed presence`
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)

r109_spirit_MR5 <- lmer(Score ~ Scale * Country * `Porosity Vignettes` * `Sensed presence`
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)

r110_spirit_MR5 <- lmer(Score ~ Scale * Country * `Porosity Scale` * `Sensed presence`
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)

r111_spirit_MR5 <- lmer(Score ~ Scale * Country * `Porosity Vignettes` * `Sensed presence`
                        + Gender + Age
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)

r112_spirit_MR5 <- lmer(Score ~ Scale * Country * `Porosity Scale` * `Sensed presence`
                        + Gender + Age
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)
```

```{r}
r105_spirit_MR6 <- lmer(Score ~ Scale * `Altered states of consciousness (b)`
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)

r108_spirit_MR6 <- lmer(Score ~ Scale * Country * `Altered states of consciousness (b)`
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)

r109_spirit_MR6 <- lmer(Score ~ Scale * Country * `Porosity Vignettes` * `Altered states of consciousness (b)`
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)

r110_spirit_MR6 <- lmer(Score ~ Scale * Country * `Porosity Scale` * `Altered states of consciousness (b)`
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)

r111_spirit_MR6 <- lmer(Score ~ Scale * Country * `Porosity Vignettes` * `Altered states of consciousness (b)`
                        + Gender + Age
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)

r112_spirit_MR6 <- lmer(Score ~ Scale * Country * `Porosity Scale` * `Altered states of consciousness (b)`
                        + Gender + Age
                        + (1 | subject_id),
                        control = lmerControl(optimizer = "bobyqa"),
                        data = d4_abs_subscales)
```


```{r, results = "hide"}
table_s40s41_subscales_MR1 <- stargazer(r105_spirit_MR1, r108_spirit_MR1, 
                                        r109_spirit_MR1, r110_spirit_MR1,
                                        r111_spirit_MR1, r112_spirit_MR1,
                                        type = "html",
                                        ci = T,
                                        report = c("vcsp"),
                                        digits = 2,
                                        digits.extra = 1000,
                                        omit.table.layout = "n",
                                        order = c("^Synesthesia$",
                                                  "^`Porosity Vignettes`$",
                                                  "^`Porosity Scale`$",
                                                  "^`Porosity Vignettes`:Synesthesia$",
                                                  "^`Porosity Scale`:Synesthesia$"),
                                        # dep.var.labels.include = F,
                                        dep.var.caption = "",
                                        single.row = T) 

cat(sub_interact_fun(table_s40s41_subscales_MR1), 
    file = "./stargazer_tables/table_s40s41_subscales_MR1.html")
```

```{r}
htmltools::includeHTML("./stargazer_tables/table_s40s41_subscales_MR1.html")
```

```{r, results = "hide"}
table_s40s41_subscales_MR2 <- stargazer(r105_spirit_MR2, r108_spirit_MR2, 
                                        r109_spirit_MR2, r110_spirit_MR2,
                                        r111_spirit_MR2, r112_spirit_MR2,
                                        type = "html",
                                        ci = T,
                                        report = c("vcsp"),
                                        digits = 2,
                                        digits.extra = 1000,
                                        omit.table.layout = "n",
                                        order = c("^`Aesthetic involvement in nature`$",
                                                  "^`Porosity Vignettes`$",
                                                  "^`Porosity Scale`$",
                                                  "^`Porosity Vignettes`:`Aesthetic involvement in nature`$",
                                                  "^`Porosity Scale`:`Aesthetic involvement in nature`$"),
                                        # dep.var.labels.include = F,
                                        dep.var.caption = "",
                                        single.row = T) 

cat(sub_interact_fun(table_s40s41_subscales_MR2), 
    file = "./stargazer_tables/table_s40s41_subscales_MR2.html")
```

```{r}
htmltools::includeHTML("./stargazer_tables/table_s40s41_subscales_MR2.html")
```

```{r, results = "hide"}
table_s40s41_subscales_MR3 <- stargazer(r105_spirit_MR3, r108_spirit_MR3, 
                                        r109_spirit_MR3, r110_spirit_MR3,
                                        r111_spirit_MR3, r112_spirit_MR3,
                                        type = "html",
                                        ci = T,
                                        report = c("vcsp"),
                                        digits = 2,
                                        digits.extra = 1000,
                                        omit.table.layout = "n",
                                        order = c("^`Altered states of consciousness \\(a\\)`$",
                                                  "^`Porosity Vignettes`$",
                                                  "^`Porosity Scale`$",
                                                  "^`Porosity Vignettes`:`Altered states of consciousness \\(a\\)`$",
                                                  "^`Porosity Scale`:`Altered states of consciousness \\(a\\)`$"),
                                        # dep.var.labels.include = F,
                                        dep.var.caption = "",
                                        single.row = T) 

cat(sub_interact_fun(table_s40s41_subscales_MR3), 
    file = "./stargazer_tables/table_s40s41_subscales_MR3.html")
```

```{r}
htmltools::includeHTML("./stargazer_tables/table_s40s41_subscales_MR3.html")
```

```{r, results = "hide"}
table_s40s41_subscales_MR4 <- stargazer(r105_spirit_MR4, r108_spirit_MR4, 
                                        r109_spirit_MR4, r110_spirit_MR4,
                                        r111_spirit_MR4, r112_spirit_MR4,
                                        type = "html",
                                        ci = T,
                                        report = c("vcsp"),
                                        digits = 2,
                                        digits.extra = 1000,
                                        omit.table.layout = "n",
                                        order = c("^`Imaginative involvement`$",
                                                  "^`Porosity Vignettes`$",
                                                  "^`Porosity Scale`$",
                                                  "^`Porosity Vignettes`:`Imaginative involvement`$",
                                                  "^`Porosity Scale`:`Imaginative involvement`$"),
                                        # dep.var.labels.include = F,
                                        dep.var.caption = "",
                                        single.row = T) 

cat(sub_interact_fun(table_s40s41_subscales_MR4), 
    file = "./stargazer_tables/table_s40s41_subscales_MR4.html")
```

```{r}
htmltools::includeHTML("./stargazer_tables/table_s40s41_subscales_MR4.html")
```

```{r, results = "hide"}
table_s40s41_subscales_MR5 <- stargazer(r105_spirit_MR5, r108_spirit_MR5, 
                                        r109_spirit_MR5, r110_spirit_MR5,
                                        r111_spirit_MR5, r112_spirit_MR5,
                                        type = "html",
                                        ci = T,
                                        report = c("vcsp"),
                                        digits = 2,
                                        digits.extra = 1000,
                                        omit.table.layout = "n",
                                        order = c("^`Sensed presence`$",
                                                  "^`Porosity Vignettes`$",
                                                  "^`Porosity Scale`$",
                                                  "^`Porosity Vignettes`:`Sensed presence`$",
                                                  "^`Porosity Scale`:`Sensed presence`$"),
                                        # dep.var.labels.include = F,
                                        dep.var.caption = "",
                                        single.row = T) 

cat(sub_interact_fun(table_s40s41_subscales_MR5), 
    file = "./stargazer_tables/table_s40s41_subscales_MR5.html")
```

```{r}
htmltools::includeHTML("./stargazer_tables/table_s40s41_subscales_MR5.html")
```

```{r, results = "hide"}
table_s40s41_subscales_MR6 <- stargazer(r105_spirit_MR6, r108_spirit_MR6, 
                                        r109_spirit_MR6, r110_spirit_MR6,
                                        r111_spirit_MR6, r112_spirit_MR6,
                                        type = "html",
                                        ci = T,
                                        report = c("vcsp"),
                                        digits = 2,
                                        digits.extra = 1000,
                                        omit.table.layout = "n",
                                        order = c("^`Altered states of consciousness \\(b\\)`$",
                                                  "^`Porosity Vignettes`$",
                                                  "^`Porosity Scale`$",
                                                  "^`Porosity Vignettes`:`Altered states of consciousness \\(b\\)`$",
                                                  "^`Porosity Scale`:`Altered states of consciousness \\(b\\)`$"),
                                        # dep.var.labels.include = F,
                                        dep.var.caption = "",
                                        single.row = T) 

cat(sub_interact_fun(table_s40s41_subscales_MR6), 
    file = "./stargazer_tables/table_s40s41_subscales_MR6.html")
```

```{r}
htmltools::includeHTML("./stargazer_tables/table_s40s41_subscales_MR6.html")
```






